Systems and methods for playback device management

ABSTRACT

Systems and methods for managing playback devices in accordance with embodiments of the invention are illustrated. One embodiment includes a method for modifying a system that includes several devices. The method includes steps for measuring a first signal pattern for wireless signals between the several devices, measuring a second signal pattern for the wireless signals after measuring the first signal pattern between the several devices, determining an updated state of the system based on a difference between the second signal pattern and the first signal pattern, and modifying state variables of one or more devices of the playback system based on the determined updated state.

CROSS-REFERENCE TO RELATED APPLICATIONS

The current application claims the benefit of and priority under 35U.S.C. § 119(e) to U.S. Provisional Patent Application No. 62/907,367entitled “Systems and Methods for Device Localization and Prediction”filed Sep. 27, 2019. The disclosure of U.S. Provisional PatentApplication No. 62/907,367 is hereby incorporated by reference in itsentirety for all purposes.

TECHNICAL FIELD

The present technology relates to consumer goods and, more particularly,to methods, systems, products, features, services, and other elementsdirected to management of playback devices in media playback systems orsome aspect thereof.

BACKGROUND

Options for accessing and listening to digital audio in an out-loudsetting were limited until in 2003, when SONOS, Inc. filed for one ofits first patent applications, entitled “Method for Synchronizing AudioPlayback between Multiple Networked Devices,” and began offering a mediaplayback system for sale in 2005. The SONOS Wireless HiFi System enablespeople to experience music from many sources via one or more networkedplayback devices. Through a software control application installed on asmartphone, tablet, or computer, one can play what he or she wants inany room that has a networked playback device. Additionally, using acontroller, for example, different songs can be streamed to each roomthat has a playback device, rooms can be grouped together forsynchronous playback, or the same song can be heard in all roomssynchronously. Given the ever-growing interest in digital media, therecontinues to be a need to develop consumer-accessible technologies tofurther enhance the listening experience.

SUMMARY

Systems and methods for managing playback devices in accordance withembodiments of the invention are illustrated. One embodiment includes amethod for modifying a system that includes several devices. The methodincludes steps for measuring a first signal pattern for wireless signalsbetween the several devices, measuring a second signal pattern for thewireless signals after measuring the first signal pattern between theseveral devices, determining an updated state of the system based on adifference between the second signal pattern and the first signalpattern, and modifying state variables of one or more devices of theplayback system based on the determined updated state.

In a further embodiment, the first signal pattern is a baseline signalpattern for a space between the several devices, where the baselinesignal pattern includes a signal pattern measured at a particular timeof day.

In still another embodiment, determining an updated state of the systemincludes estimating positions of a set of one or more individuals in aspace between the several devices based on the difference between thesecond signal pattern and the first signal pattern, and modifying thestate variables of the devices of the system is based on the estimatedpositions of the set of individuals in the space.

In a still further embodiment, the method further includes steps forlearning location information for signal patterns, wherein estimatingpositions of the set of individuals in the space is based on the learnedlocation information.

In yet another embodiment, learning location information for signalpatterns comprises measuring several signal patterns of the space atmultiple time instances, localizing an individual in the space at eachtime instance, and associating a location of the individual with thecorresponding signal pattern, wherein estimating the positions of theset of individuals comprises matching the second signal pattern to aparticular signal pattern of the several signal patterns, and estimatinga location for the set of individuals based on at least one associatedlocation for the particular signal pattern.

In a yet further embodiment, localizing an individual includeslocalizing a portable device associated with the individual.

In another additional embodiment, localizing an individual includesreceiving input from the individual that indicates a location of theindividual within the space.

One embodiment includes a non-transitory machine readable mediumcontaining processor instructions for managing a system includes severaldevices, where execution of the instructions by a processor causes theprocessor to perform a process comprising receiving informationindicative of a first signal pattern for wireless signals between theseveral devices, receiving information indicative of a second signalpattern for the wireless signals between the several devices,determining an updated state of the system based on a difference betweenthe second signal pattern and the first signal pattern, and modifyingstate variables of one or more devices of the system based on thedetermined updated state.

In a further additional embodiment, the method further includes stepsfor monitoring motion in a space between the several devices, whereinthe first signal pattern is measured when there is no motion measured inthe space.

In another embodiment again, modifying the system includes modifying aset of one or more parameters for audio content provided at the severaldevices, wherein the set of parameters includes at least one of thegroup consisting of equalizer settings, volume, bass, treble, balance,and fade.

In a further embodiment again, the first signal pattern is a baselinesignal pattern for a space between the several devices, wherein themethod further includes periodically updating the baseline pattern.

In still yet another embodiment, updating the baseline pattern includescomputing an average pattern from signal strengths measured at varioustimes of day.

In a still yet further embodiment, updating the baseline patterncomprises detecting a lack of activity in the system, measuring a thirdpattern of wireless signals between the several devices, and updatingthe baseline pattern with the third pattern.

In still another additional embodiment, the several devices include acenter speaker device, a right speaker device, and a left speakerdevice.

One embodiment includes a device of a playback system that includesseveral devices, the device comprising a network interface, a set of oneor more processors, and a non-transitory machine readable mediumcontaining processor instructions. Execution of the instructions by aprocessor causes the processor to perform a process comprising receivinginformation indicative of a first signal pattern for wireless signalsbetween the several devices, receiving information indicative of asecond signal pattern for the wireless signals between the severaldevices, determining an updated state of the system based on adifference between the second signal pattern and the first signalpattern, and modifying the system based on the determined updated state.

In a still further additional embodiment, determining an updated stateincludes detecting a change in at least one of the group consisting of alocation and orientation of at least one playback device of the severalplayback devices.

In still another embodiment again, the several playback devices includeat least two primary playback devices and a set of one or more secondaryplayback devices, wherein the first and second signal patterns includessignal strengths measured between each of the at least two primaryplayback devices and the set of secondary playback devices, whereindetecting a change includes determining a repositioning of the set ofsecondary playback devices.

In a still further embodiment again, the several devices include aprimary playback device and a set of one or more secondary playbackdevices, wherein the first and second signal patterns includes signalstrengths measured between at least one radio chain of the primarydevice and each of several radio chains on each of the secondaryplayback devices, wherein detecting a change includes determining arepositioning of the set of secondary playback devices.

In yet another additional embodiment, modifying the playback systemcomprises determining whether the difference exceeds a threshold, whenthe difference exceeds a threshold, performing a recalibration process,and when the difference does not exceed a threshold, providing aninstruction to reposition at least one playback device of the playbacksystem.

In a yet further additional embodiment, measuring the first and secondsignal patterns includes capturing a statistical measure of wirelesssignal strengths over a period of time.

In yet another embodiment again, modifying the playback system comprisesdetermining a predicted target action based on a machine learning model,wherein the machine learning model is trained based on states of theplayback system and a history of device interactions, and performing thepredicted target action.

Additional embodiments and features are set forth in part in thedescription that follows, and in part will become apparent to thoseskilled in the art upon examination of the specification or may belearned by the practice of the invention. A further understanding of thenature and advantages of the present invention may be realized byreference to the remaining portions of the specification and thedrawings, which forms a part of this disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

Features, aspects, and advantages of the presently disclosed technologymay be better understood with regard to the following description,appended claims, and accompanying drawings.

FIG. 1A is a partial cutaway view of an environment having a mediaplayback system configured in accordance with aspects of the disclosedtechnology.

FIG. 1B is a schematic diagram of the media playback system of FIG. 1Aand one or more networks.

FIG. 2A is a functional block diagram of an example playback device.

FIG. 2B is an isometric diagram of an example housing of the playbackdevice of FIG. 2A.

FIGS. 3A-3E are diagrams showing example playback device configurationsin accordance with aspects of the disclosure.

FIG. 4A is a functional block diagram of an example controller device inaccordance with aspects of the disclosure.

FIGS. 4B and 4C are controller interfaces in accordance with aspects ofthe disclosure.

FIG. 5 is a functional block diagram of certain components of an examplenetwork microphone device in accordance with aspects of the disclosure.

FIG. 6A is a diagram of an example voice input.

FIG. 6B is a graph depicting an example sound specimen in accordancewith aspects of the disclosure.

FIG. 7 conceptually illustrates an example of a process for localizing aportable device in a networked sensor system in accordance with anembodiment.

FIG. 8 illustrates an example of a data structure for characteristics ofsignals in a networked sensor system in accordance with an embodiment.

FIG. 9 illustrates an example of differences in signals based on signaldirection between two devices in accordance with an embodiment.

FIG. 10 illustrates an example of changing probabilities for portabledevice moving between reference devices in accordance with anembodiment.

FIG. 11 illustrates an example of localizing a portable device in anetworked sensor system in accordance with an embodiment.

FIG. 12 illustrates an example of a localization element that localizesportable devices in accordance with an embodiment.

FIG. 13 illustrates an example of a localization application inaccordance with an embodiment.

FIG. 14 conceptually illustrates a process for playback devicemanagement in accordance with an embodiment.

FIG. 15 illustrates an example of a layout element for determininglayouts of a system and/or updating a system based on a determinedlayout in accordance with an embodiment.

FIG. 16 illustrates an example of a layout application for determininglayouts of a system and/or updating a system based on a determinedlayout in accordance with an embodiment.

FIG. 17 illustrates an example of determining a layout of a system witha reference device in accordance with an embodiment.

FIG. 18 illustrates an example of signal strength patterns for a systemwith reference devices measured in accordance with an embodiment.

FIG. 19 illustrates an example of determining a layout of a system withmultiple-antenna devices in accordance with an embodiment.

FIG. 20 illustrates an example of signal strength patterns for aspeakers with multiple antennas measured in accordance with anembodiment.

FIG. 21 illustrates an example of determining a space state inaccordance with an embodiment.

FIG. 22 illustrates example charts of signal patterns measured for aperson in different positions in space.

FIG. 23 illustrates charts of signal strength patterns with differentsurround positions.

FIG. 24 conceptually illustrates an example of a process for training aprediction model in accordance with an embodiment.

FIG. 25 illustrates an example of a prediction element that trains andpredicts target devices in accordance with an embodiment.

FIG. 26 illustrates an example of a prediction application in accordancewith an embodiment.

FIGS. 27A-B illustrate examples of a graphical user interface (GUI)based on predicted target devices.

The drawings are for purposes of illustrating example embodiments, butit should be understood that these embodiments are not limited to thearrangements and instrumentality shown in the drawings. In the drawings,identical reference numbers identify at least generally similarelements. To facilitate the discussion of any particular element, themost significant digit or digits of any reference number refers to theFigure in which that element is first introduced. For example, element103 a is first introduced and discussed with reference to FIG. 1A.

DETAILED DESCRIPTION I. Overview

As devices become smarter, it is becoming increasingly desirable tolocate and interact with devices within indoor areas (e.g., a home).However, due to the variety of different floorplans and variousinterfering effects found indoors (e.g., walls, appliances, furniture,etc.), it can be difficult to locate a device in such an environment.While some conventional solutions have used wireless signals to locate adevice based on signal strength of a wireless signal from a single knownsource (e.g., a BLUETOOTH beacon, WI-FI transmitter, etc.) detected bythe device, such solutions can often be inaccurate or inconsistent,varying based on the transmitter/receiver hardware in the device and/orthe BLUETOOTH beacon. For example, a first device may appear to becloser to the BLUETOOTH beacon than a second device because the firstdevice detected a wireless signal transmitted by the BLUETOOTH beacon ata higher power level than the second device. However, the first devicemay simply employ a high gain antenna and, in fact, be significantlyfurther away from the BLUETOOTH beacon than the second device. Further,some conventional systems require specific calibration for anenvironment, which can be difficult and tedious, particularly as theposition of devices in the system change. In contrast to theseconventional methods, some embodiments of the technology describedherein may be employed to advantageously measure and normalize signalsbetween a portable device and reference devices (e.g., speakers, NMDs,controllers, etc.) in a system (e.g., a media playback system (MPS)) toestimate, for each reference device, a likelihood that the portabledevice is located near the reference device. As a result, locationsidentified in accordance with the techniques described herein can beconsiderably more accurate, without the need for calibration oradjustments for different environments, relative to locations identifiedusing conventional approaches.

Wireless playback devices provide portability and flexibility in a waythat was previously difficult to achieve. Wireless playback devices canbe used in multiple different functions, e.g., as satellite speakers ina home theater (HT) system, as standalone portable speakers, etc. As apart of a HT system, each speaker may have specific settings and roleswithin the system (e.g., as a right-channel satellite speaker) which canresult in poor performance when the speakers are incorrectly positioned(e.g., when left/right speakers are placed in the opposite positions).In some embodiments, processes can measure wireless signals between thewireless speakers and other devices in an MPS to determine the layout(e.g., positions, orientations, etc.) of the devices in the system.Processes in accordance with numerous embodiments can modify a systembased on the determined layouts in a variety of ways, including (but notlimited to) providing notifications to a user to modify the layout,modifying parameters for devices in the system, etc.

In many situations, it can be beneficial to be able to determine a spacestate (e.g., a number of individuals in a space, positions ofindividuals in the space, etc.) of the space between devices of asystem. For example, in a HT system, if the positions of individuals inthe space can be determined, various audio settings (e.g., balance,fade, volume, etc.) can be adjusted to optimize the sound experience forthe individuals. Processes in accordance with various embodiments can beused to determine space states based on measured signal patterns betweenthe devices. In certain embodiments, signal patterns indicate therelative strengths of signals between each of multiple devices in asystem.

In a number of embodiments, measured signal patterns can be comparedwith calibration signal patterns that are recorded during a calibrationsession, where signal patterns can be measured and associated with a“true” location for an individual. True locations in accordance withseveral embodiments can be determined through various localizationtechniques such as (but not limited to) those described throughout thisapplication, via external sensor systems (e.g., cameras, motion sensors,etc.), and/or manual location information received via user inputs.

In some cases, it can be desirable for the interaction of devices to beautomated or streamlined based on the location of a portable device(e.g., a mobile phone). It can also be difficult to identify a desiredtarget device to interact with based on the location of the portabledevice, not only because the location of the portable device can bedifficult to determine, but also because the location of the portabledevice alone may not provide sufficient context to correctly predict thedesired target device. For example, a user may always turn on thekitchen SONOS speakers first thing in the morning from their bedroom,even when they have a closer set of speakers in their bedroom, becausethey intend to go to the kitchen. Conventional solutions use simplerules-based heuristics based on manual programming by a user, but suchsolutions can be difficult to maintain and are often unable to adjust toa user's changing preferences. In contrast to these conventionalmethods, some embodiments of the technology described herein may beemployed to advantageously generate training data based on a user'spatterns in order to train a target device prediction model specific toa user (or household). Such processes can result in more accuratepredictions with less manual user input. In particular, the processesmay enable a system to identify repeated behaviors in user behavior andadapt to the identified user behavior to enhance subsequent userinteractions with the system.

Although many of the examples described herein refer to MPSs, oneskilled in the art will recognize that similar systems and methods canbe used in a variety of different systems to locate, predict targetdevices, and/or train such a predictor, including (but not limited to)security systems, Internet of Things (IoT) systems, etc., withoutdeparting from the scope of the present disclosure. Further, thetechniques described herein may be advantageously employed for devicelocalization in any of a variety of operating environments includingindoor environments, outdoor environments, and mixed indoor-outdoorenvironments.

While some embodiments described herein may refer to functions performedby given actors, such as “users” and/or other entities, it should beunderstood that this description is for purposes of explanation only.The claims should not be interpreted to require action by any suchexample actor unless explicitly required by the language of the claimsthemselves.

II. Example Operating Environment

FIGS. 1A and 1B illustrate an example configuration of a media playbacksystem 100 (or “MPS 100”) in which one or more embodiments disclosedherein may be implemented. Referring first to FIG. 1A, the MPS 100 asshown is associated with an example home environment having a pluralityof rooms and spaces, which may be collectively referred to as a “homeenvironment,” “smart home,” or “environment 101.” The environment 101comprises a household having several rooms, spaces, and/or playbackzones, including a master bathroom 101 a, a master bedroom 101 b(referred to herein as “Nick's Room”), a second bedroom 101 c, a familyroom or den 101 d, an office 101 e, a living room 101 f, a dining room101 g, a kitchen 101 h, and an outdoor patio 101 i. While certainembodiments and examples are described below in the context of a homeenvironment, the technologies described herein may be implemented inother types of environments. In some embodiments, for example, the MPS100 can be implemented in one or more commercial settings (e.g., arestaurant, mall, airport, hotel, a retail or other store), one or morevehicles (e.g., a sports utility vehicle, bus, car, a ship, a boat, anairplane), multiple environments (e.g., a combination of home andvehicle environments), and/or another suitable environment wheremulti-zone audio may be desirable.

Within these rooms and spaces, the MPS 100 includes one or morecomputing devices. Referring to FIGS. 1A and 1B together, such computingdevices can include playback devices 102 (identified individually asplayback devices 102 a-102 o), network microphone devices 103(identified individually as “NMDs” 103 a-102 i), and controller devices104 a and 104 b (collectively “controller devices 104”). Referring toFIG. 1B, the home environment may include additional and/or othercomputing devices, including local network devices, such as one or moresmart illumination devices 108 (FIG. 1 ), a smart thermostat 110, and alocal computing device 105 (FIG. 1A). In embodiments described below,one or more of the various playback devices 102 may be configured asportable playback devices, while others may be configured as stationaryplayback devices. For example, the headphones 102 o (FIG. 1B) are aportable playback device, while the playback device 102 d on thebookcase may be a stationary device. As another example, the playbackdevice 102 c on the Patio may be a battery-powered device, which mayallow it to be transported to various areas within the environment 101,and outside of the environment 101, when it is not plugged in to a walloutlet or the like. Localization, prediction, and/or training ofprediction models in accordance with a number of embodiments can beperformed on such computing devices.

With reference still to FIG. 1B, the various playback, networkmicrophone, and controller devices 102-104 and/or other network devicesof the MPS 100 may be coupled to one another via point-to-pointconnections and/or over other connections, which may be wired and/orwireless, via a LAN 111 including a network router 109. For example, theplayback device 102 j in the Den 101 d (FIG. 1A), which may bedesignated as the “Left” device, may have a point-to-point connectionwith the playback device 102 a, which is also in the Den 101 d and maybe designated as the “Right” device. In a related embodiment, the Leftplayback device 102 j may communicate with other network devices, suchas the playback device 102 b, which may be designated as the “Front”device, via a point-to-point connection and/or other connections via theLAN 111.

As further shown in FIG. 1B, the MPS 100 may be coupled to one or moreremote computing devices 106 via a wide area network (“WAN”) 107. Insome embodiments, each remote computing device 106 may take the form ofone or more cloud servers. The remote computing devices 106 may beconfigured to interact with computing devices in the environment 101 invarious ways. For example, the remote computing devices 106 may beconfigured to facilitate streaming and/or controlling playback of mediacontent, such as audio, in the home environment 101.

In some implementations, the various playback devices, NMDs, and/orcontroller devices 102-104 may be communicatively coupled to at leastone remote computing device associated with a voice activated system(“VAS”) and at least one remote computing device associated with a mediacontent service (“MCS”). For instance, in the illustrated example ofFIG. 1B , remote computing devices 106 a are associated with a VAS 190and remote computing devices 106 b are associated with an MCS 192.Although only a single VAS 190 and a single MCS 192 are shown in theexample of FIG. 1B for purposes of clarity, the MPS 100 may be coupledto multiple, different VASes and/or MCSes. In some implementations,VASes may be operated by one or more of AMAZON, GOOGLE, APPLE,MICROSOFT, SONOS or other voice assistant providers. In someimplementations, MCSes may be operated by one or more of SPOTIFY,PANDORA, AMAZON MUSIC, or other media content services.

As further shown in FIG. 1B, the remote computing devices 106 furtherinclude remote computing device 106 c configured to perform certainoperations, such as remotely facilitating media playback functions,managing device and system status information, directing communicationsbetween the devices of the MPS 100 and one or multiple VASes and/orMCSes, among other operations. In one example, the remote computingdevices 106 c provide cloud servers for one or more SONOS Wireless HiFiSystems. Remote computing devices can be used for parts of localization,prediction, and/or training of prediction models in accordance with anumber of embodiments.

In various implementations, one or more of the playback devices 102 maytake the form of or include an on-board (e.g., integrated) networkmicrophone device. For example, the playback devices 102 a-e include orare otherwise equipped with corresponding NMDs 103 a-e, respectively. Aplayback device that includes or is equipped with an NMD may be referredto herein interchangeably as a playback device or an NMD unlessindicated otherwise in the description. In some cases, one or more ofthe NMDs 103 may be a stand-alone device. For example, the NMDs 103 fand 103 g may be stand-alone devices. A stand-alone NMD may omitcomponents and/or functionality that is typically included in a playbackdevice, such as a speaker or related electronics. For instance, in suchcases, a stand-alone NMD may not produce audio output or may producelimited audio output (e.g., relatively low-quality audio output).

The various playback and network microphone devices 102 and 103 of theMPS 100 may each be associated with a unique name, which may be assignedto the respective devices by a user, such as during setup of one or moreof these devices. For instance, as shown in the illustrated example ofFIG. 1B, a user may assign the name “Bookcase” to playback device 102 dbecause it is physically situated on a bookcase. Similarly, the NMD 103f may be assigned the named “Island” because it is physically situatedon an island countertop in the Kitchen 101 h (FIG. 1A). Some playbackdevices may be assigned names according to a zone or room, such as theplayback devices 102 e, 102 l, 102 m, and 102 n, which are named“Bedroom,” “Dining Room,” “Living Room,” and “Office,” respectively.Further, certain playback devices may have functionally descriptivenames. For example, the playback devices 102 a and 102 b are assignedthe names “Right” and “Front,” respectively, because these two devicesare configured to provide specific audio channels during media playbackin the zone of the Den 101 d (FIG. 1A). The playback device 102 c in thePatio may be named portable because it is battery-powered and/or readilytransportable to different areas of the environment 101. Other namingconventions are possible.

As discussed above, an NMD may detect and process sound from itsenvironment, such as sound that includes background noise mixed withspeech spoken by a person in the NMD's vicinity. For example, as soundsare detected by the NMD in the environment, the NMD may process thedetected sound to determine if the sound includes speech that containsvoice input intended for the NMD and ultimately a particular VAS. Forexample, the NMD may identify whether speech includes a wake wordassociated with a particular VAS.

In the illustrated example of FIG. 1B, the NMDs 103 are configured tointeract with the VAS 190 over a network via the LAN 111 and the router109. Interactions with the VAS 190 may be initiated, for example, whenan NMD identifies in the detected sound a potential wake word. Theidentification causes a wake-word event, which in turn causes the NMD tobegin transmitting detected-sound data to the VAS 190. In someimplementations, the various local network devices 102-105 (FIG. 1A)and/or remote computing devices 106 c of the MPS 100 may exchangevarious feedback, information, instructions, and/or related data withthe remote computing devices associated with the selected VAS. Suchexchanges may be related to or independent of transmitted messagescontaining voice inputs. In some embodiments, the remote computingdevice(s) and the media playback system 100 may exchange data viacommunication paths as described herein and/or using a metadata exchangechannel as described in U.S. Application Publication No.US-2017-0242653, and titled “Voice Control of a Media Playback System,”which is herein incorporated by reference in its entirety.

Upon receiving the stream of sound data, the VAS 190 determines if thereis voice input in the streamed data from the NMD, and if so the VAS 190will also determine an underlying intent in the voice input. The VAS 190may next transmit a response back to the MPS 100, which can includetransmitting the response directly to the NMD that caused the wake-wordevent. The response is typically based on the intent that the VAS 190determined was present in the voice input. As an example, in response tothe VAS 190 receiving a voice input with an utterance to “Play Hey Judeby The Beatles,” the VAS 190 may determine that the underlying intent ofthe voice input is to initiate playback and further determine thatintent of the voice input is to play the particular song “Hey Jude.”After these determinations, the VAS 190 may transmit a command to aparticular MCS 192 to retrieve content (i.e., the song “Hey Jude”), andthat MCS 192, in turn, provides (e.g., streams) this content directly tothe MPS 100 or indirectly via the VAS 190. In some implementations, theVAS 190 may transmit to the MPS 100 a command that causes the MPS 100itself to retrieve the content from the MCS 192.

In certain implementations, NMDs may facilitate arbitration amongst oneanother when voice input is identified in speech detected by two or moreNMDs located within proximity of one another. For example, theNMD-equipped playback device 102 d in the environment 101 (FIG. 1A) isin relatively close proximity to the NMD-equipped Living Room playbackdevice 102 m, and both devices 102 d and 102 m may at least sometimesdetect the same sound. In such cases, this may require arbitration as towhich device is ultimately responsible for providing detected-sound datato the remote VAS. Examples of arbitrating between NMDs may be found,for example, in previously referenced U.S. Application Publication No.US-2017-0242653.

In certain implementations, an NMD may be assigned to, or otherwiseassociated with, a designated or default playback device that may notinclude an NMD. For example, the Island NMD 103 f in the Kitchen 101 h(FIG. 1A) may be assigned to the Dining Room playback device 102 l,which is in relatively close proximity to the Island NMD 103 f. Inpractice, an NMD may direct an assigned playback device to play audio inresponse to a remote VAS receiving a voice input from the NMD to playthe audio, which the NMD might have sent to the VAS in response to auser speaking a command to play a certain song, album, playlist, etc.Additional details regarding assigning NMDs and playback devices asdesignated or default devices may be found, for example, in previouslyreferenced U.S. Application Publication No. US-2017-0242653.

Further aspects relating to the different components of the example MPS100 and how the different components may interact to provide a user witha media experience may be found in the following sections. Whilediscussions herein may generally refer to the example MPS 100,technologies described herein are not limited to applications within,among other things, the home environment described above. For instance,the technologies described herein may be useful in other homeenvironment configurations comprising more or fewer of any of theplayback, network microphone, and/or controller devices 102-104. Forexample, the technologies herein may be utilized within an environmenthaving a single playback device 102 and/or a single NMD 103. In someexamples of such cases, the LAN 111 (FIG. 1B) may be eliminated and thesingle playback device 102 and/or the single NMD 103 may communicatedirectly with the remote computing devices 106 a-d. In some embodiments,a telecommunication network (e.g., an LTE network, a 5G network, etc.)may communicate with the various playback, network microphone, and/orcontroller devices 102-104 independent of a LAN.

While specific implementations of MPS's have been described above withrespect to FIGS. 1A and 1B, there are numerous configurations of MPS's,including, but not limited to, those that do not interact with remoteservices, systems that do not include controllers, and/or any otherconfiguration as appropriate to the requirements of a given application.

a. Example Playback & Network Microphone Devices

FIG. 2A is a functional block diagram illustrating certain aspects ofone of the playback devices 102 of the MPS 100 of FIGS. 1A and 1B. Asshown, the playback device 102 includes various components, each ofwhich is discussed in further detail below, and the various componentsof the playback device 102 may be operably coupled to one another via asystem bus, communication network, or some other connection mechanism.In the illustrated example of FIG. 2A, the playback device 102 may bereferred to as an “NMD-equipped” playback device because it includescomponents that support the functionality of an NMD, such as one of theNMDs 103 shown in FIG. 1A.

As shown, the playback device 102 includes at least one processor 212,which may be a clock-driven computing component configured to processinput data according to instructions stored in memory 213. The memory213 may be a tangible, non-transitory, computer-readable mediumconfigured to store instructions that are executable by the processor212. For example, the memory 213 may be data storage that can be loadedwith software code 214 that is executable by the processor 212 toachieve certain functions.

In one example, these functions may involve the playback device 102retrieving audio data from an audio source, which may be anotherplayback device. In another example, the functions may involve theplayback device 102 sending audio data, detected-sound data (e.g.,corresponding to a voice input), and/or other information to anotherdevice on a network via at least one network interface 224. In yetanother example, the functions may involve the playback device 102causing one or more other playback devices to synchronously playbackaudio with the playback device 102. In yet a further example, thefunctions may involve the playback device 102 facilitating being pairedor otherwise bonded with one or more other playback devices to create amulti-channel audio environment. Numerous other example functions arepossible, some of which are discussed below.

As just mentioned, certain functions may involve the playback device 102synchronizing playback of audio content with one or more other playbackdevices. During synchronous playback, a listener may not perceivetime-delay differences between playback of the audio content by thesynchronized playback devices. U.S. Pat. No. 8,234,395 filed on Apr. 4,2004, and titled “System and method for synchronizing operations among aplurality of independently clocked digital data processing devices,”which is hereby incorporated by reference in its entirety, provides inmore detail some examples for audio playback synchronization amongplayback devices.

To facilitate audio playback, the playback device 102 includes audioprocessing components 216 that are generally configured to process audioprior to the playback device 102 rendering the audio. In this respect,the audio processing components 216 may include one or moredigital-to-analog converters (“DAC”), one or more audio preprocessingcomponents, one or more audio enhancement components, one or moredigital signal processors (“DSPs”), and so on. In some implementations,one or more of the audio processing components 216 may be a subcomponentof the processor 212. In operation, the audio processing components 216receive analog and/or digital audio and process and/or otherwiseintentionally alter the audio to produce audio signals for playback.

The produced audio signals may then be provided to one or more audioamplifiers 217 for amplification and playback through one or morespeakers 218 operably coupled to the amplifiers 217. The audioamplifiers 217 may include components configured to amplify audiosignals to a level for driving one or more of the speakers 218.

Each of the speakers 218 may include an individual transducer (e.g., a“driver”) or the speakers 218 may include a complete speaker systeminvolving an enclosure with one or more drivers. A particular driver ofa speaker 218 may include, for example, a subwoofer (e.g., for lowfrequencies), a mid-range driver (e.g., for middle frequencies), and/ora tweeter (e.g., for high frequencies). In some cases, a transducer maybe driven by an individual corresponding audio amplifier of the audioamplifiers 217. In some implementations, a playback device may notinclude the speakers 218, but instead may include a speaker interfacefor connecting the playback device to external speakers. In certainembodiments, a playback device may include neither the speakers 218 northe audio amplifiers 217, but instead may include an audio interface(not shown) for connecting the playback device to an external audioamplifier or audio-visual receiver.

In addition to producing audio signals for playback by the playbackdevice 102, the audio processing components 216 may be configured toprocess audio to be sent to one or more other playback devices, via thenetwork interface 224, for playback. In example scenarios, audio contentto be processed and/or played back by the playback device 102 may bereceived from an external source, such as via an audio line-in interface(e.g., an auto-detecting 3.5 mm audio line-in connection) of theplayback device 102 (not shown) or via the network interface 224, asdescribed below.

As shown, the at least one network interface 224, may take the form ofone or more wireless interfaces 225 and/or one or more wired interfaces226. A wireless interface may provide network interface functions forthe playback device 102 to wirelessly communicate with other devices(e.g., other playback device(s), NMD(s), and/or controller device(s)) inaccordance with a communication protocol (e.g., any wireless standardincluding IEEE 802.11a, 802.11b, 802.11g, 802.11n, 802.11ac, 802.15, 4Gmobile communication standard, and so on). A wired interface may providenetwork interface functions for the playback device 102 to communicateover a wired connection with other devices in accordance with acommunication protocol (e.g., IEEE 802.3). While the network interface224 shown in FIG. 2A includes both wired and wireless interfaces, theplayback device 102 may in some implementations include only wirelessinterface(s) or only wired interface(s).

In general, the network interface 224 facilitates data flow between theplayback device 102 and one or more other devices on a data network. Forinstance, the playback device 102 may be configured to receive audiocontent over the data network from one or more other playback devices,network devices within a LAN, and/or audio content sources over a WAN,such as the Internet. In one example, the audio content and othersignals transmitted and received by the playback device 102 may betransmitted in the form of digital packet data comprising an InternetProtocol (P)-based source address and IP-based destination addresses. Insuch a case, the network interface 224 may be configured to parse thedigital packet data such that the data destined for the playback device102 is properly received and processed by the playback device 102.

As shown in FIG. 2A, the playback device 102 also includes voiceprocessing components 220 that are operably coupled to one or moremicrophones 222. The microphones 222 are configured to detect sound(i.e., acoustic waves) in the environment of the playback device 102,which is then provided to the voice processing components 220. Morespecifically, each microphone 222 is configured to detect sound andconvert the sound into a digital or analog signal representative of thedetected sound, which can then cause the voice processing component 220to perform various functions based on the detected sound, as describedin greater detail below. In one implementation, the microphones 222 arearranged as an array of microphones (e.g., an array of six microphones).In some implementations, the playback device 102 includes more than sixmicrophones (e.g., eight microphones or twelve microphones) or fewerthan six microphones (e.g., four microphones, two microphones, or asingle microphones).

In operation, the voice-processing components 220 are generallyconfigured to detect and process sound received via the microphones 222,identify potential voice input in the detected sound, and extractdetected-sound data to enable a VAS, such as the VAS 190 (FIG. 1 ), toprocess voice input identified in the detected-sound data. The voiceprocessing components 220 may include one or more analog-to-digitalconverters, an acoustic echo canceller (“AEC”), a spatial processor(e.g., one or more multi-channel Wiener filters, one or more otherfilters, and/or one or more beam former components), one or more buffers(e.g., one or more circular buffers), one or more wake-word engines, oneor more voice extractors, and/or one or more speech processingcomponents (e.g., components configured to recognize a voice of aparticular user or a particular set of users associated with ahousehold), among other example voice processing components. In exampleimplementations, the voice processing components 220 may include orotherwise take the form of one or more DSPs or one or more modules of aDSP. In this respect, certain voice processing components 220 may beconfigured with particular parameters (e.g., gain and/or spectralparameters) that may be modified or otherwise tuned to achieveparticular functions. In some implementations, one or more of the voiceprocessing components 220 may be a subcomponent of the processor 212.

In some implementations, the voice-processing components 220 may detectand store a user's voice profile, which may be associated with a useraccount of the MPS 100. For example, voice profiles may be stored asand/or compared to variables stored in a set of command information ordata table. The voice profile may include aspects of the tone orfrequency of a user's voice and/or other unique aspects of the user'svoice, such as those described in previously-referenced U.S. ApplicationPublication No. US-2017-0242653.

As further shown in FIG. 2A, the playback device 102 also includes powercomponents 227. The power components 227 can include at least anexternal power source interface 228, which may be coupled to a powersource (not shown) via a power cable or the like that physicallyconnects the playback device 102 to an electrical outlet or some otherexternal power source. Other power components may include, for example,transformers, converters, and like components configured to formatelectrical power.

In some implementations, the power components 227 of the playback device102 may additionally include an internal power source 229 (e.g., one ormore batteries) configured to power the playback device 102 without aphysical connection to an external power source. When equipped with theinternal power source 229, the playback device 102 may operateindependent of an external power source. In some such implementations,the external power source interface 228 may be configured to facilitatecharging the internal power source 229. As discussed before, a playbackdevice comprising an internal power source may be referred to herein asa “portable playback device.” On the other hand, a playback device thatoperates using an external power source may be referred to herein as a“stationary playback device,” although such a device may in fact bemoved around a home or other environment.

The playback device 102 can further include a user interface 240 thatmay facilitate user interactions independent of or in conjunction withuser interactions facilitated by one or more of the controller devices104. In various embodiments, the user interface 240 includes one or morephysical buttons and/or supports graphical interfaces provided on touchsensitive screen(s) and/or surface(s), among other possibilities, for auser to directly provide input. The user interface 240 may furtherinclude one or more of lights (e.g., LEDs) and the speakers to providevisual and/or audio feedback to a user.

As an illustrative example, FIG. 2B shows an example housing 230 of theplayback device 102 that includes a user interface in the form of acontrol area 232 at a top portion 234 of the housing 230. The controlarea 232 includes buttons 236 a-c for controlling audio playback, volumelevel, and other functions. The control area 232 also includes a button236 d for toggling the microphones 222 to either an on state or an offstate.

As further shown in FIG. 2B, the control area 232 is at least partiallysurrounded by apertures formed in the top portion 234 of the housing 230through which the microphones 222 (not visible in FIG. 2B) receive thesound in the environment of the playback device 102. The microphones 222may be arranged in various positions along and/or within the top portion234 or other areas of the housing 230 so as to detect sound from one ormore directions relative to the playback device 102.

While specific implementations of playback and network microphonedevices have been described above with respect to FIGS. 2A and 2B, thereare numerous configurations of devices, including, but not limited to,those having no UI, microphones in different locations, multiplemicrophone arrays positioned in different arrangements, and/or any otherconfiguration as appropriate to the requirements of a given application.For example, UIs and/or microphone arrays can be implemented in otherplayback devices and/or computing devices rather than those describedherein. Further, although a specific example of playback device 102 isdescribed with reference to MPS 100, one skilled in the art willrecognize that playback devices as described herein can be used in avariety of different environments, including (but not limited to)environments with more and/or fewer elements, without departing from thescope of the present disclosure. Likewise, MPS's as described herein canbe used with various different playback devices.

By way of illustration, SONOS, Inc. presently offers (or has offered)for sale certain playback devices that may implement certain of theembodiments disclosed herein, including a “PLAY:1,” “PLAY:3,” “PLAY:5,”“PLAYBAR,” “CONNECT:AMP,” “PLAYBASE,” “BEAM,” “CONNECT,” and “SUB.” Anyother past, present, and/or future playback devices may additionally oralternatively be used to implement the playback devices of exampleembodiments disclosed herein. Additionally, it should be understood thata playback device is not limited to the examples illustrated in FIG. 2Aor 2B or to the SONOS product offerings. For example, a playback devicemay include, or otherwise take the form of, a wired or wirelessheadphone set, which may operate as a part of the media playback system100 via a network interface or the like. In another example, a playbackdevice may include or interact with a docking station for personalmobile media playback devices. In yet another example, a playback devicemay be integral to another device or component such as a television, alighting fixture, or some other device for indoor or outdoor use.

b. Example Playback Device Configurations

FIGS. 3A-3E show example configurations of playback devices. Referringfirst to FIG. 3A, in some example instances, a single playback devicemay belong to a zone. For example, the playback device 102 c (FIG. 1A)on the Patio may belong to Zone A. In some implementations describedbelow, multiple playback devices may be “bonded” to form a “bondedpair,” which together form a single zone. For example, the playbackdevice 102 f (FIG. 1A) named “Bed 1” in FIG. 3A may be bonded to theplayback device 102 g (FIG. 1A) named “Bed 2” in FIG. 3A to form Zone B.Bonded playback devices may have different playback responsibilities(e.g., channel responsibilities). In another implementation describedbelow, multiple playback devices may be merged to form a single zone.For example, the playback device 102 d named “Bookcase” may be mergedwith the playback device 102 m named “Living Room” to form a single ZoneC. The merged playback devices 102 d and 102 m may not be specificallyassigned different playback responsibilities. That is, the mergedplayback devices 102 d and 102 m may, aside from playing audio contentin synchrony, each play audio content as they would if they were notmerged.

For purposes of control, each zone in the MPS 100 may be represented asa single user interface (“UI”) entity. For example, as displayed by thecontroller devices 104, Zone A may be provided as a single entity named“Portable,” Zone B may be provided as a single entity named “Stereo,”and Zone C may be provided as a single entity named “Living Room.”

In various embodiments, a zone may take on the name of one of theplayback devices belonging to the zone. For example, Zone C may take onthe name of the Living Room device 102 m (as shown). In another example,Zone C may instead take on the name of the Bookcase device 102 d. In afurther example, Zone C may take on a name that is some combination ofthe Bookcase device 102 d and Living Room device 102 m. The name that ischosen may be selected by a user via inputs at a controller device 104.In some embodiments, a zone may be given a name that is different thanthe device(s) belonging to the zone. For example, Zone B in FIG. 3A isnamed “Stereo” but none of the devices in Zone B have this name. In oneaspect, Zone B is a single UI entity representing a single device named“Stereo,” composed of constituent devices “Bed 1” and “Bed 2.” In oneimplementation, the Bed 1 device may be playback device 102 f in themaster bedroom 101 h (FIG. 1A) and the Bed 2 device may be the playbackdevice 102 g also in the master bedroom 101 h (FIG. 1A).

As noted above, playback devices that are bonded may have differentplayback responsibilities, such as playback responsibilities for certainaudio channels. For example, as shown in FIG. 3B, the Bed 1 and Bed 2devices 102 f and 102 g may be bonded so as to produce or enhance astereo effect of audio content. In this example, the Bed 1 playbackdevice 102 f may be configured to play a left channel audio component,while the Bed 2 playback device 102 g may be configured to play a rightchannel audio component. In some implementations, such stereo bondingmay be referred to as “pairing.”

Additionally, playback devices that are configured to be bonded may haveadditional and/or different respective speaker drivers. As shown in FIG.3C, the playback device 102 b named “Front” may be bonded with theplayback device 102 k named “SUB.” The Front device 102 b may render arange of mid to high frequencies, and the SUB device 102 k may renderlow frequencies as, for example, a subwoofer. When unbonded, the Frontdevice 102 b may be configured to render a full range of frequencies. Asanother example, FIG. 3D shows the Front and SUB devices 102 b and 102 kfurther bonded with Right and Left playback devices 102 a and 102 j,respectively. In some implementations, the Right and Left devices 102 aand 102 j may form surround or “satellite” channels of a home theatersystem. The bonded playback devices 102 a, 102 b, 102 j, and 102 k mayform a single Zone D (FIG. 3A).

In some implementations, playback devices may also be “merged.” Incontrast to certain bonded playback devices, playback devices that aremerged may not have assigned playback responsibilities, but may eachrender the full range of audio content that each respective playbackdevice is capable of. Nevertheless, merged devices may be represented asa single UI entity (i.e., a zone, as discussed above). For instance,FIG. 3E shows the playback devices 102 d and 102 m in the Living Roommerged, which would result in these devices being represented by thesingle UI entity of Zone C. In one embodiment, the playback devices 102d and 102 m may playback audio in synchrony, during which each outputsthe full range of audio content that each respective playback device 102d and 102 m is capable of rendering.

In some embodiments, a stand-alone NMD may be in a zone by itself. Forexample, the NMD 103 h from FIG. 1A is named “Closet” and forms Zone Iin FIG. 3A. An NMD may also be bonded or merged with another device soas to form a zone. For example, the NMD device 103 f named “Island” maybe bonded with the playback device 102 i Kitchen, which together formZone F, which is also named “Kitchen.” Additional details regardingassigning NMDs and playback devices as designated or default devices maybe found, for example, in previously referenced U.S. ApplicationPublication No. US-2017-0242653. In some embodiments, a stand-alone NMDmay not be assigned to a zone.

Zones of individual, bonded, and/or merged devices may be arranged toform a set of playback devices that playback audio in synchrony. Such aset of playback devices may be referred to as a “group,” “zone group,”“synchrony group,” or “playback group.” In response to inputs providedvia a controller device 104, playback devices may be dynamically groupedand ungrouped to form new or different groups that synchronously playback audio content. For example, referring to FIG. 3A, Zone A may begrouped with Zone B to form a zone group that includes the playbackdevices of the two zones. As another example, Zone A may be grouped withone or more other Zones C-I. The Zones A-I may be grouped and ungroupedin numerous ways. For example, three, four, five, or more (e.g., all) ofthe Zones A-I may be grouped. When grouped, the zones of individualand/or bonded playback devices may play back audio in synchrony with oneanother, as described in previously referenced U.S. Pat. No. 8,234,395.Grouped and bonded devices are example types of associations betweenportable and stationary playback devices that may be caused in responseto a trigger event, as discussed above and described in greater detailbelow.

In various implementations, the zones in an environment may be assigneda particular name, which may be the default name of a zone within a zonegroup or a combination of the names of the zones within a zone group,such as “Dining Room+Kitchen,” as shown in FIG. 3A. In some embodiments,a zone group may be given a unique name selected by a user, such as“Nick's Room,” as also shown in FIG. 3A. The name “Nick's Room” may be aname chosen by a user over a prior name for the zone group, such as theroom name “Master Bedroom.”

Referring back to FIG. 2A, certain data may be stored in the memory 213as one or more state variables that are periodically updated and used todescribe the state of a playback zone, the playback device(s), and/or azone group associated therewith. The memory 213 may also include thedata associated with the state of the other devices of the mediaplayback system 100, which may be shared from time to time among thedevices so that one or more of the devices have the most recent dataassociated with the system.

In some embodiments, the memory 213 of the playback device 102 may storeinstances of various variable types associated with the states.Variables instances may be stored with identifiers (e.g., tags)corresponding to type. For example, certain identifiers may be a firsttype “a1” to identify playback device(s) of a zone, a second type “b1”to identify playback device(s) that may be bonded in the zone, and athird type “c1” to identify a zone group to which the zone may belong.As a related example, in FIG. 1A, identifiers associated with the Patiomay indicate that the Patio is the only playback device of a particularzone and not in a zone group. Identifiers associated with the LivingRoom may indicate that the Living Room is not grouped with other zonesbut includes bonded playback devices 102 a, 102 b, 102 j, and 102 k.Identifiers associated with the Dining Room may indicate that the DiningRoom is part of Dining Room+Kitchen group and that devices 103 f and 102i are bonded. Identifiers associated with the Kitchen may indicate thesame or similar information by virtue of the Kitchen being part of theDining Room+Kitchen zone group. Other example zone variables andidentifiers are described below.

In yet another example, the MPS 100 may include variables or identifiersrepresenting other associations of zones and zone groups, such asidentifiers associated with Areas, as shown in FIG. 3A. An Area mayinvolve a cluster of zone groups and/or zones not within a zone group.For instance, FIG. 3A shows a first area named “First Area” and a secondarea named “Second Area.” The First Area includes zones and zone groupsof the Patio, Den, Dining Room, Kitchen, and Bathroom. The Second Areaincludes zones and zone groups of the Bathroom, Nick's Room, Bedroom,and Living Room. In one aspect, an Area may be used to invoke a clusterof zone groups and/or zones that share one or more zones and/or zonegroups of another cluster. In this respect, such an Area differs from azone group, which does not share a zone with another zone group. Furtherexamples of techniques for implementing Areas may be found, for example,in U.S. application Ser. No. 15/682,506 filed Aug. 21, 2017 and titled“Room Association Based on Name,” and U.S. Pat. No. 8,483,853 filed Sep.11, 2007, and titled “Controlling and manipulating groupings in amulti-zone media system.” Each of these applications is incorporatedherein by reference in its entirety. In some embodiments, the MPS 100may not implement Areas, in which case the system may not storevariables associated with Areas.

The memory 213 may be further configured to store other data. Such datamay pertain to audio sources accessible by the playback device 102 or aplayback queue that the playback device (or some other playbackdevice(s)) may be associated with. In embodiments described below, thememory 213 is configured to store a set of command data for selecting aparticular VAS when processing voice inputs.

During operation, one or more playback zones in the environment of FIG.1A may each be playing different audio content. For instance, the usermay be grilling in the Patio zone and listening to hip hop music beingplayed by the playback device 102 c, while another user may be preparingfood in the Kitchen zone and listening to classical music being playedby the playback device 102 i. In another example, a playback zone mayplay the same audio content in synchrony with another playback zone. Forinstance, the user may be in the Office zone where the playback device102 n is playing the same hip-hop music that is being playing byplayback device 102 c in the Patio zone. In such a case, playbackdevices 102 c and 102 n may be playing the hip-hop in synchrony suchthat the user may seamlessly (or at least substantially seamlessly)enjoy the audio content that is being played out-loud while movingbetween different playback zones. Synchronization among playback zonesmay be achieved in a manner similar to that of synchronization amongplayback devices, as described in previously referenced U.S. Pat. No.8,234,395.

As suggested above, the zone configurations of the MPS 100 may bedynamically modified. As such, the MPS 100 may support numerousconfigurations. For example, if a user physically moves one or moreplayback devices to or from a zone, the MPS 100 may be reconfigured toaccommodate the change(s). For instance, if the user physically movesthe playback device 102 c from the Patio zone to the Office zone, theOffice zone may now include both the playback devices 102 c and 102 n.In some cases, the user may pair or group the moved playback device 102c with the Office zone and/or rename the players in the Office zoneusing, for example, one of the controller devices 104 and/or voiceinput. As another example, if one or more playback devices 102 are movedto a particular space in the home environment that is not already aplayback zone, the moved playback device(s) may be renamed or associatedwith a playback zone for the particular space.

Further, different playback zones of the MPS 100 may be dynamicallycombined into zone groups or split up into individual playback zones.For example, the Dining Room zone and the Kitchen zone may be combinedinto a zone group for a dinner party such that playback devices 102 iand 102 l may render audio content in synchrony. As another example,bonded playback devices in the Den zone may be split into (i) atelevision zone and (ii) a separate listening zone. The television zonemay include the Front playback device 102 b. The listening zone mayinclude the Right, Left, and SUB playback devices 102 a, 102 j, and 102k, which may be grouped, paired, or merged, as described above.Splitting the Den zone in such a manner may allow one user to listen tomusic in the listening zone in one area of the living room space, andanother user to watch the television in another area of the living roomspace. In a related example, a user may utilize either of the NMD 103 aor 103 b (FIG. 1B) to control the Den zone before it is separated intothe television zone and the listening zone. Once separated, thelistening zone may be controlled, for example, by a user in the vicinityof the NMD 103 a, and the television zone may be controlled, forexample, by a user in the vicinity of the NMD 103 b. As described above,however, any of the NMDs 103 may be configured to control the variousplayback and other devices of the MPS 100.

c. Example Controller Devices

FIG. 4A is a functional block diagram illustrating certain aspects of aselected one of the controller devices 104 of the MPS 100 of FIG. 1A.Controller devices in accordance with several embodiments can be used invarious systems, such as (but not limited to) an MPS as described inFIG. 1A. Such controller devices may also be referred to herein as a“control device” or “controller.” The controller device shown in FIG. 4Amay include components that are generally similar to certain componentsof the network devices described above, such as a processor 412, memory413 storing program software 414, at least one network interface 424,and one or more microphones 422. In one example, a controller device maybe a dedicated controller for the MPS 100. In another example, acontroller device may be a network device on which media playback systemcontroller application software may be installed, such as for example,an iPhone™, iPad™ or any other smart phone, tablet, or network device(e.g., a networked computer such as a PC or Mac™).

The memory 413 of the controller device 104 may be configured to storecontroller application software and other data associated with the MPS100 and/or a user of the system 100. The memory 413 may be loaded withinstructions in software 414 that are executable by the processor 412 toachieve certain functions, such as facilitating user access, control,and/or configuration of the MPS 100. The controller device 104 can beconfigured to communicate with other network devices via the networkinterface 424, which may take the form of a wireless interface, asdescribed above.

In one example, system information (e.g., such as a state variable) maybe communicated between the controller device 104 and other devices viathe network interface 424. For instance, the controller device 104 mayreceive playback zone and zone group configurations in the MPS 100 froma playback device, an NMD, or another network device. Likewise, thecontroller device 104 may transmit such system information to a playbackdevice or another network device via the network interface 424. In somecases, the other network device may be another controller device.

The controller device 104 may also communicate playback device controlcommands, such as volume control and audio playback control, to aplayback device via the network interface 424. As suggested above,changes to configurations of the MPS 100 may also be performed by a userusing the controller device 104. The configuration changes may includeadding/removing one or more playback devices to/from a zone,adding/removing one or more zones to/from a zone group, forming a bondedor merged player, separating one or more playback devices from a bondedor merged player, among others.

As shown in FIG. 4A, the controller device 104 can also include a userinterface 440 that is generally configured to facilitate user access andcontrol of the MPS 100. The user interface 440 may include atouch-screen display or other physical interface configured to providevarious graphical controller interfaces, such as the controllerinterfaces 440 a and 440 b shown in FIGS. 4B and 4C. Referring to FIGS.4B and 4C together, the controller interfaces 440 a and 440 b include aplayback control region 442, a playback zone region 443, a playbackstatus region 444, a playback queue region 446, and a sources region448. The user interface as shown is just one example of an interfacethat may be provided on a network device, such as the controller deviceshown in FIG. 4A, and accessed by users to control a media playbacksystem, such as the MPS 100. Other user interfaces of varying formats,styles, and interactive sequences may alternatively be implemented onone or more network devices to provide comparable control access to amedia playback system.

The playback control region 442 (FIG. 4B) may include selectable icons(e.g., by way of touch or by using a cursor) that, when selected, causeplayback devices in a selected playback zone or zone group to play orpause, fast forward, rewind, skip to next, skip to previous, enter/exitshuffle mode, enter/exit repeat mode, enter/exit cross fade mode, etc.The playback control region 442 may also include selectable icons that,when selected, modify equalization settings and/or playback volume,among other possibilities.

The playback zone region 443 (FIG. 4C) may include representations ofplayback zones within the MPS 100. The playback zones regions 443 mayalso include a representation of zone groups, such as the DiningRoom+Kitchen zone group, as shown. In some embodiments, the graphicalrepresentations of playback zones may be selectable to bring upadditional selectable icons to manage or configure the playback zones inthe MIPS 100, such as a creation of bonded zones, creation of zonegroups, separation of zone groups, and renaming of zone groups, amongother possibilities.

For example, as shown, a “group” icon may be provided within each of thegraphical representations of playback zones. The “group” icon providedwithin a graphical representation of a particular zone may be selectableto bring up options to select one or more other zones in the MIPS 100 tobe grouped with the particular zone. Once grouped, playback devices inthe zones that have been grouped with the particular zone will beconfigured to play audio content in synchrony with the playbackdevice(s) in the particular zone. Analogously, a “group” icon may beprovided within a graphical representation of a zone group. In thiscase, the “group” icon may be selectable to bring up options to deselectone or more zones in the zone group to be removed from the zone group.Other interactions and implementations for grouping and ungrouping zonesvia a user interface are also possible. The representations of playbackzones in the playback zone region 443 (FIG. 4C) may be dynamicallyupdated as playback zone or zone group configurations are modified.

The playback status region 444 (FIG. 4B) may include graphicalrepresentations of audio content that is presently being played,previously played, or scheduled to play next in the selected playbackzone or zone group. The selected playback zone or zone group may bevisually distinguished on a controller interface, such as within theplayback zone region 443 and/or the playback status region 444. Thegraphical representations may include track title, artist name, albumname, album year, track length, and/or other relevant information thatmay be useful for the user to know when controlling the MIPS 100 via acontroller interface.

The playback queue region 446 may include graphical representations ofaudio content in a playback queue associated with the selected playbackzone or zone group. In some embodiments, each playback zone or zonegroup may be associated with a playback queue comprising informationcorresponding to zero or more audio items for playback by the playbackzone or zone group. For instance, each audio item in the playback queuemay comprise a uniform resource identifier (URI), a uniform resourcelocator (URL), or some other identifier that may be used by a playbackdevice in the playback zone or zone group to find and/or retrieve theaudio item from a local audio content source or a networked audiocontent source, which may then be played back by the playback device.

In one example, a playlist may be added to a playback queue, in whichcase information corresponding to each audio item in the playlist may beadded to the playback queue. In another example, audio items in aplayback queue may be saved as a playlist. In a further example, aplayback queue may be empty, or populated but “not in use” when theplayback zone or zone group is playing continuously streamed audiocontent, such as Internet radio that may continue to play untilotherwise stopped, rather than discrete audio items that have playbackdurations. In an alternative embodiment, a playback queue can includeInternet radio and/or other streaming audio content items and be “inuse” when the playback zone or zone group is playing those items. Otherexamples are also possible.

When playback zones or zone groups are “grouped” or “ungrouped,”playback queues associated with the affected playback zones or zonegroups may be cleared or re-associated. For example, if a first playbackzone including a first playback queue is grouped with a second playbackzone including a second playback queue, the established zone group mayhave an associated playback queue that is initially empty, that containsaudio items from the first playback queue (such as if the secondplayback zone was added to the first playback zone), that contains audioitems from the second playback queue (such as if the first playback zonewas added to the second playback zone), or a combination of audio itemsfrom both the first and second playback queues. Subsequently, if theestablished zone group is ungrouped, the resulting first playback zonemay be re-associated with the previous first playback queue or may beassociated with a new playback queue that is empty or contains audioitems from the playback queue associated with the established zone groupbefore the established zone group was ungrouped. Similarly, theresulting second playback zone may be re-associated with the previoussecond playback queue or may be associated with a new playback queuethat is empty or contains audio items from the playback queue associatedwith the established zone group before the established zone group wasungrouped. Other examples are also possible.

With reference still to FIGS. 4B and 4C, the graphical representationsof audio content in the playback queue region 446 (FIG. 4B) may includetrack titles, artist names, track lengths, and/or other relevantinformation associated with the audio content in the playback queue. Inone example, graphical representations of audio content may beselectable to bring up additional selectable icons to manage and/ormanipulate the playback queue and/or audio content represented in theplayback queue. For instance, a represented audio content may be removedfrom the playback queue, moved to a different position within theplayback queue, or selected to be played immediately, or after anycurrently playing audio content, among other possibilities. A playbackqueue associated with a playback zone or zone group may be stored in amemory on one or more playback devices in the playback zone or zonegroup, on a playback device that is not in the playback zone or zonegroup, and/or some other designated device. Playback of such a playbackqueue may involve one or more playback devices playing back media itemsof the queue, perhaps in sequential or random order.

The sources region 448 may include graphical representations ofselectable audio content sources and/or selectable voice assistantsassociated with a corresponding VAS. The VASes may be selectivelyassigned. In some examples, multiple VASes, such as AMAZON's Alexa,MICROSOFT's Cortana, etc., may be invokable by the same NMD. In someembodiments, a user may assign a VAS exclusively to one or more NMDs.For example, a user may assign a first VAS to one or both of the NMDs102 a and 102 b in the Living Room shown in FIG. 1A, and a second VAS tothe NMD 103 f in the Kitchen. Other examples are possible.

d. Example Audio Content Sources

The audio sources in the sources region 448 may be audio content sourcesfrom which audio content may be retrieved and played by the selectedplayback zone or zone group. One or more playback devices in a zone orzone group may be configured to retrieve for playback audio content(e.g., according to a corresponding URI or URL for the audio content)from a variety of available audio content sources. In one example, audiocontent may be retrieved by a playback device directly from acorresponding audio content source (e.g., via a line-in connection). Inanother example, audio content may be provided to a playback device overa network via one or more other playback devices or network devices. Asdescribed in greater detail below, in some embodiments audio content maybe provided by one or more media content services.

Example audio content sources may include a memory of one or moreplayback devices in a media playback system such as the MPS 100 of FIG.1 , local music libraries on one or more network devices (e.g., acontroller device, a network-enabled personal computer, or anetworked-attached storage (“NAS”)), streaming audio services providingaudio content via the Internet (e.g., cloud-based music services), oraudio sources connected to the media playback system via a line-in inputconnection on a playback device or network device, among otherpossibilities.

In some embodiments, audio content sources may be added or removed froma media playback system such as the MPS 100 of FIG. 1A. In one example,an indexing of audio items may be performed whenever one or more audiocontent sources are added, removed, or updated. Indexing of audio itemsmay involve scanning for identifiable audio items in allfolders/directories shared over a network accessible by playback devicesin the media playback system and generating or updating an audio contentdatabase comprising metadata (e.g., title, artist, album, track length,among others) and other associated information, such as a URI or URL foreach identifiable audio item found. Other examples for managing andmaintaining audio content sources may also be possible.

e. Example Network Microphone Devices

FIG. 5 is a functional block diagram showing an NMD 503 configured inaccordance with embodiments of the disclosure. The NMD 503 includesvoice capture components (“VCC”, or collectively “voice processor 560”),a wake-word engine 570, and at least one voice extractor 572, each ofwhich can be operably coupled to the voice processor 560. The NMD 503further includes the microphones 222 and the at least one networkinterface 224 described above and may also include other components,such as audio amplifiers, interface, etc., which are not shown in FIG. 5for purposes of clarity.

The microphones 222 of the NMD 503 can be configured to provide detectedsound, S_(D), from the environment of the NMD 503 to the voice processor560. The detected sound S_(D) may take the form of one or more analog ordigital signals. In example implementations, the detected sound S_(D)may be composed of a plurality of signals associated with respectivechannels 562 that are fed to the voice processor 560.

Each channel 562 may correspond to a particular microphone 222. Forexample, an NMD having six microphones may have six correspondingchannels. Each channel of the detected sound S_(D) may bear certainsimilarities to the other channels but may differ in certain regards,which may be due to the position of the given channel's correspondingmicrophone relative to the microphones of other channels. For example,one or more of the channels of the detected sound S_(D) may have agreater signal to noise ratio (“SNR”) of speech to background noise thanother channels.

As further shown in FIG. 5 , the voice processor 560 includes an AEC564, a spatial processor 566, and one or more buffers 568. In operation,the AEC 564 receives the detected sound S_(D) and filters or otherwiseprocesses the sound to suppress echoes and/or to otherwise improve thequality of the detected sound S_(D). That processed sound may then bepassed to the spatial processor 566.

The spatial processor 566 is typically configured to analyze thedetected sound S_(D) and identify certain characteristics, such as asound's amplitude (e.g., decibel level), frequency spectrum,directionality, etc. In one respect, the spatial processor 566 may helpfilter or suppress ambient noise in the detected sound S_(D) frompotential user speech based on similarities and differences in theconstituent channels 562 of the detected sound S_(D), as discussedabove. As one possibility, the spatial processor 566 may monitor metricsthat distinguish speech from other sounds. Such metrics can include, forexample, energy within the speech band relative to background noise andentropy within the speech band—a measure of spectral structure—which istypically lower in speech than in most common background noise. In someimplementations, the spatial processor 566 may be configured todetermine a speech presence probability, examples of such functionalityare disclosed in U.S. patent application Ser. No. 15/984,073, filed May18, 2018, titled “Linear Filtering for Noise-Suppressed SpeechDetection,” and U.S. patent application Ser. No. 16/147,710, filed Sep.29, 2018, and titled “Linear Filtering for Noise-Suppressed SpeechDetection via Multiple Network Microphone Devices,” each of which isincorporated herein by reference in its entirety.

The wake-word engine 570 can be configured to monitor and analyzereceived audio to determine if any wake words are present in the audio.The wake-word engine 570 may analyze the received audio using a wakeword detection algorithm. If the wake-word engine 570 detects a wakeword, a network microphone device may process voice input contained inthe received audio. Example wake word detection algorithms accept audioas input and provide an indication of whether a wake word is present inthe audio. Many first- and third-party wake word detection processes areknown and commercially available. For instance, operators of a voiceservice may make their processes available for use in third-partydevices. Alternatively, a process may be trained to detect certainwake-words.

In some embodiments, the wake-word engine 570 runs multiple wake worddetection processes on the received audio simultaneously (orsubstantially simultaneously). As noted above, different voice services(e.g. AMAZON's Alexa®, APPLE's Siri®, MICROSOFT's Cortana®, GOOGLE'SAssistant, etc.) each use a different wake word for invoking theirrespective voice service. To support multiple services, the wake-wordengine 570 may run the received audio through the wake word detectionprocess for each supported voice service in parallel. In suchembodiments, the network microphone device 103 may include VAS selectorcomponents 574 configured to pass voice input to the appropriate voiceassistant service. In other embodiments, the VAS selector components 574may be omitted. In some embodiments, individual NMDs 103 of the MPS 100may be configured to run different wake word detection processesassociated with particular VASes. For example, the NMDs of playbackdevices 102 a and 102 b of the Living Room may be associated withAMAZON's ALEXA®, and be configured to run a corresponding wake worddetection process (e.g., configured to detect the wake word “Alexa” orother associated wake word), while the NMD of playback device 102 f inthe Kitchen may be associated with GOOGLE's Assistant, and be configuredto run a corresponding wake word detection process (e.g., configured todetect the wake word “OK, Google” or other associated wake word).

In some embodiments, a network microphone device may include speechprocessing components configured to further facilitate voice processing,such as by performing voice recognition trained to recognize aparticular user or a particular set of users associated with ahousehold. Voice recognition software may implement processes that aretuned to specific voice profile(s).

In operation, the one or more buffers 568—one or more of which may bepart of or separate from the memory 213 (FIG. 2A)—capture datacorresponding to the detected sound S_(D). More specifically, the one ormore buffers 568 capture detected-sound data that was processed by theupstream AEC 564 and spatial processor 566.

In general, the detected-sound data form a digital representation (i.e.,sound-data stream), S_(DS), of the sound detected by the microphones222. In practice, the sound-data stream S_(DS) may take a variety offorms. As one possibility, the sound-data stream S_(DS) may be composedof frames, each of which may include one or more sound samples. Theframes may be streamed (i.e., read out) from the one or more buffers 568for further processing by downstream components, such as the wake-wordengine 570 and the voice extractor 572 of the NMD 503.

In some implementations, at least one buffer 568 captures detected-sounddata utilizing a sliding window approach in which a given amount (i.e.,a given window) of the most recently captured detected-sound data isretained in the at least one buffer 568 while older detected-sound dataare overwritten when they fall outside of the window. For example, atleast one buffer 568 may temporarily retain 20 frames of a soundspecimen at given time, discard the oldest frame after an expirationtime, and then capture a new frame, which is added to the 19 priorframes of the sound specimen.

In practice, when the sound-data stream S_(DS) is composed of frames,the frames may take a variety of forms having a variety ofcharacteristics. As one possibility, the frames may take the form ofaudio frames that have a certain resolution (e.g., 16 bits ofresolution), which may be based on a sampling rate (e.g., 44,100 Hz).Additionally, or alternatively, the frames may include informationcorresponding to a given sound specimen that the frames define, such asmetadata that indicates frequency response, power input level,signal-to-noise ratio, microphone channel identification, and/or otherinformation of the given sound specimen, among other examples. Thus, insome embodiments, a frame may include a portion of sound (e.g., one ormore samples of a given sound specimen) and metadata regarding theportion of sound. In other embodiments, a frame may only include aportion of sound (e.g., one or more samples of a given sound specimen)or metadata regarding a portion of sound.

The voice processor 560 can also include at least one lookback buffer569, which may be part of or separate from the memory 213 (FIG. 2A). Inoperation, the lookback buffer 569 can store sound metadata that isprocessed based on the detected-sound data S_(D) received from themicrophones 222. As noted above, the microphones 224 can include aplurality of microphones arranged in an array. The sound metadata caninclude, for example: (1) frequency response data for individualmicrophones of the array, (2) an echo return loss enhancement measure(i.e., a measure of the effectiveness of the acoustic echo canceller(AEC) for each microphone), (3) a voice direction measure; (4)arbitration statistics (e.g., signal and noise estimates for the spatialprocessing streams associated with different microphones); and/or (5)speech spectral data (i.e., frequency response evaluated on processedaudio output after acoustic echo cancellation and spatial processinghave been performed). Other sound metadata may also be used to identifyand/or classify noise in the detected-sound data S_(D). In at least someembodiments, the sound metadata may be transmitted separately from thesound-data stream S_(DS), as reflected in the arrow extending from thelookback buffer 569 to the network interface 224. For example, the soundmetadata may be transmitted from the lookback buffer 569 to one or moreremote computing devices separate from the VAS which receives thesound-data stream S_(DS). In some embodiments, for example, the metadatacan be transmitted to a remote service provider for analysis toconstruct or modify a noise classifier, as described in more detailbelow.

In any case, components of the NMD 503 downstream of the voice processor560 may process the sound-data stream S_(DS). For instance, thewake-word engine 570 can be configured to apply one or moreidentification processes to the sound-data stream S_(DS) (e.g., streamedsound frames) to spot potential wake words in the detected-sound S_(D).When the wake-word engine 570 spots a potential wake word, the wake-wordengine 570 can provide an indication of a “wake-word event” (alsoreferred to as a “wake-word trigger”) to the voice extractor 572 in theform of signal S_(W).

In response to the wake-word event (e.g., in response to a signal S_(W)from the wake-word engine 570 indicating the wake-word event), the voiceextractor 572 can be configured to receive and format (e.g., packetize)the sound-data stream S_(DS). For instance, the voice extractor 572packetizes the frames of the sound-data stream S_(DS) into messages. Thevoice extractor 572 can transmit or stream these messages, M_(V), thatmay contain voice input in real time or near real time, to a remote VAS,such as the VAS 190 (FIG. 1 ), via the network interface 218.

The VAS can be configured to process the sound-data stream S_(DS)contained in the messages M_(V) sent from the NMD 503. Morespecifically, the VAS can be configured to identify voice input based onthe sound-data stream S_(DS). Referring to FIG. 6A, a voice input 680may include a wake-word portion 680 a and an utterance portion 680 b.The wake-word portion 680 a can correspond to detected sound that causedthe wake-word event. For instance, the wake-word portion 680 a cancorrespond to detected sound that caused the wake-word engine 570 toprovide an indication of a wake-word event to the voice extractor 572.The utterance portion 680 b can correspond to detected sound thatpotentially comprises a user request following the wake-word portion 680a.

As an illustrative example, FIG. 6B shows an example first soundspecimen. In this example, the sound specimen corresponds to thesound-data stream S_(DS) (e.g., one or more audio frames) associatedwith the spotted wake word 680 a of FIG. 6A. As illustrated, the examplefirst sound specimen comprises sound detected in the playback device 102i's environment (i) immediately before a wake word was spoken, which maybe referred to as a pre-roll portion (between times to and t₁), (ii)while the wake word was spoken, which may be referred to as a wake-meterportion (between times t₁ and t₂), and/or (iii) after the wake word wasspoken, which may be referred to as a post-roll portion (between timest₂ and t₃). Other sound specimens are also possible.

Typically, the VAS may first process the wake-word portion 680 a withinthe sound-data stream S_(DS) to verify the presence of the wake word. Insome instances, the VAS may determine that the wake-word portion 680 acomprises a false wake word (e.g., the word “Election” when the word“Alexa” is the target wake word). In such an occurrence, the VAS maysend a response to the NMD 503 (FIG. 5 ) with an indication for the NMD503 to cease extraction of sound data, which may cause the voiceextractor 572 to cease further streaming of the detected-sound data tothe VAS. The wake-word engine 570 may resume or continue monitoringsound specimens until another potential wake word, leading to anotherwake-word event. In some implementations, the VAS may not process orreceive the wake-word portion 680 a but instead processes only theutterance portion 680 b.

In any case, the VAS processes the utterance portion 680 b to identifythe presence of any words in the detected-sound data and to determine anunderlying intent from these words. The words may correspond to acertain command and certain keywords 684 (identified individually inFIG. 6A as a first keyword 684 a and a second keyword 684 b). A keywordmay be, for example, a word in the voice input 680 identifying aparticular device or group in the MPS 100. For instance, in theillustrated example, the keywords 684 may be one or more wordsidentifying one or more zones in which the music is to be played, suchas the Living Room and the Dining Room (FIG. 1A).

To determine the intent of the words, the VAS is typically incommunication with one or more databases associated with the VAS (notshown) and/or one or more databases (not shown) of the MPS 100. Suchdatabases may store various user data, analytics, catalogs, and otherinformation for natural language processing and/or other processing. Insome implementations, such databases may be updated for adaptivelearning and feedback for a neural network based on voice-inputprocessing. In some cases, the utterance portion 680 b may includeadditional information, such as detected pauses (e.g., periods ofnon-speech) between words spoken by a user, as shown in FIG. 6A. Thepauses may demarcate the locations of separate commands, keywords, orother information spoke by the user within the utterance portion 680 b.

Based on certain command criteria, the VAS may take actions as a resultof identifying one or more commands in the voice input, such as thecommand 682. Command criteria may be based on the inclusion of certainkeywords within the voice input, among other possibilities.Additionally, or alternatively, command criteria for commands mayinvolve identification of one or more control-state and/or zone-statevariables in conjunction with identification of one or more particularcommands. Control-state variables may include, for example, indicatorsidentifying a level of volume, a queue associated with one or moredevices, and playback state, such as whether devices are playing aqueue, paused, etc. Zone-state variables may include, for example,indicators identifying which, if any, zone players are grouped.

After processing the voice input, the VAS may send a response to the MPS100 with an instruction to perform one or more actions based on anintent it determined from the voice input. For example, based on thevoice input, the VAS may direct the MPS 100 to initiate playback on oneor more of the playback devices 102, control one or more of thesedevices (e.g., raise/lower volume, group/ungroup devices, etc.), turnon/off certain smart devices, among other actions. After receiving theresponse from the VAS, the wake-word engine 570 the NMD 503 may resumeor continue to monitor the sound-data stream S_(DS) until it spotsanother potential wake-word, as discussed above.

Referring back to FIG. 5 , in multi-VAS implementations, the NMD 503 mayinclude a VAS selector 574 (shown in dashed lines) that is generallyconfigured to direct the voice extractor's extraction and transmissionof the sound-data stream S_(DS) to the appropriate VAS when a givenwake-word is identified by a particular wake-word engine, such as thefirst wake-word engine 570 a, the second wake-word engine 570 b, or theadditional wake-word engine 571. In such implementations, the NMD 503may include multiple, different wake-word engines and/or voiceextractors, each supported by a particular VAS. Similar to thediscussion above, each wake-word engine may be configured to receive asinput the sound-data stream S_(DS) from the one or more buffers 568 andapply identification algorithms to cause a wake-word trigger for theappropriate VAS. Thus, as one example, the first wake-word engine 570 amay be configured to identify the wake word “Alexa” and cause the NMD503 to invoke the AMAZON VAS when “Alexa” is spotted. As anotherexample, the second wake-word engine 570 b may be configured to identifythe wake word “Ok, Google” and cause the NMD 503 to invoke the GOOGLEVAS when “Ok, Google” is spotted. In single-VAS implementations, the VASselector 574 may be omitted.

In additional or alternative implementations, the NMD 503 may includeother voice-input identification engines 571 (shown in dashed lines)that enable the NMD 503 to operate without the assistance of a remoteVAS. As an example, such an engine may identify in detected soundcertain commands (e.g., “play,” “pause,” “turn on,” etc.) and/or certainkeywords or phrases, such as the unique name assigned to a givenplayback device (e.g., “Bookcase,” “Patio,” “Office,” etc.). In responseto identifying one or more of these commands, keywords, and/or phrases,the NMD 503 may communicate a signal (not shown in FIG. 5 ) that causesthe audio processing components 216 (FIG. 2A) to perform one or moreactions. For instance, when a user says “Hey Sonos, stop the music inthe office,” the NMD 503 may communicate a signal to the office playbackdevice 102 n, either directly, or indirectly via one or more otherdevices of the MPS 100, which causes the office device 102 n to stopaudio playback. Reducing or eliminating the need for assistance from aremote VAS may reduce latency that might otherwise occur when processingvoice input remotely. In some cases, the identification algorithmsemployed may be configured to identify commands that are spoken withouta preceding wake word. For instance, in the example above, the NMD 503may employ an identification algorithm that triggers an event to stopthe music in the office without the user first saying “Hey Sonos” oranother wake word.

III. Localizing a Portable Device

Systems and methods in accordance with numerous embodiments can be usedto localize portable devices in networked device systems. Unlike otherlocation technologies, processes in accordance with some embodiments canmaintain control over both the roaming device (i.e., the portable devicethat is being moved around) and the reference devices (e.g., stationaryplayback devices, controllers, etc.). As a result, a greater level ofcontrol over the devices within the device ecosystem can be leveraged todetermine a relative location for a portable device with a high degreeof accuracy in challenging environments (e.g., indoor environments withall types of obstructions).

In certain embodiments, localizing a portable device can be performed atthe portable device that is being located and/or at reference devices(e.g., a coordinator device) in an MPS. Localization processes inaccordance with several embodiments can be distributed across multipledevices (e.g., portable devices, stationary devices, remote devices,etc.). In several embodiments, a coordinator device is designated tocollect and store signal information from reference devices and/or fromthe portable device. Coordinator devices in accordance with numerousembodiments can be selected from the available devices of an MPS basedon one or more of several factors, including (but not limited to) RSSIof signals received from the portable device at the reference devices,frequency of use, device specifications (e.g., number of processorcores, processor clock speed, processor cache size, non-volatile memorysize, volatile memory size, etc.). For example, a particular player canbe selected as a coordinator device based on how long the processor hasbeen idle, so as not to interfere with the operation of any otherdevices during playback (e.g., selecting a speaker sitting in a guestbedroom that is used infrequently). In certain embodiments, coordinatordevices can include the portable device that is being located.

In a number of embodiments, localizing a portable device (e.g., aportable playback device, a smartphone, a tablet, etc.) can be used toidentify a relative location for the portable device based on a numberof reference devices in an MPS. Reference devices in accordance with anumber of embodiments can include stationary devices and/or portabledevices. As the localizing of a portable device is not an absolutelocation, but rather a location relative to the locations of otherreference devices, processes in accordance with many embodiments can beused to determine a nearest device, even when one or more of thereference devices is also portable.

An example of a process for localizing a portable device in a networkedsensor system in accordance with an embodiment is conceptuallyillustrated in FIG. 7 . Process 700 identifies (705) characteristics ofsignals transmitted between reference devices. Process 700 alsoidentifies (710) characteristics of signals transmitted between aportable device and the reference devices.

In certain embodiments, signals are transmitted when each referencedevice (e.g., network players, NMDs, etc.) and/or portable deviceperforms a wireless (e.g., WI-FI) scan. Wireless scans in accordancewith numerous embodiments can include broadcasting a first wirelesssignal that causes other wireless devices to respond with a secondsignal. In a number of embodiments, wireless radios in each device canprovide, as a result of a wireless scan, signal information, which caninclude (but is not limited to) an indication of which devicesresponded, an indication of how long ago the scan was performed/how longago a device responded, and/or received signal strength indicator (RSSI)values associated with the response from a particular device. Signalinformation in accordance with certain embodiments is gathered in pairsbetween all of the devices.

Processes in accordance with certain embodiments can scan periodically,allowing the devices to maintain a history of signals received from theother devices. Reference and/or portable devices in accordance withseveral embodiments can scan for known devices and collectcharacteristics (e.g., RSSI) in a buffer (e.g., a ring buffer) andcalculate statistics (e.g., weighted averages, variances, etc.) based ona history of collected signal characteristics. In some embodiments,signal characteristics and/or calculated statistics can be identified byeach of the devices, pre-processed, and transmitted to a coordinatordevice. Coordinator devices in accordance with various embodiments cancollect and store signal information from reference devices and/or theportable device. In many embodiments, identified signal characteristicsand/or calculated statistics of the signals can be stored in a matrix,that stores values for a given characteristic (e.g., RSSI).

An example of a matrix data structure for signal strength in a system isillustrated in FIG. 8 . In this example, data structure 800 includesvarious data that can be used to store various system and signalinformation. Data structure 800 includes matrix 805 (“ra_mean”), withRSSI measurements from each of five reference devices. Matrix 805includes five rows and six columns, where the intersection of each rowand column represents a RSSI for a signal between a transmittingreference device (across the top of the matrix) and a receivingreference device (down the side of the matrix). The sixth columncontains values for signals from the portable device to each of the fivereference devices. The diagonals of the matrix are zeros (as a devicedoes not transmit a signal to itself). In many cases, matrices caninclude measurements between all devices of a networked system. Forlarge networks, systems in accordance with numerous embodiments can berepresented by a sparse matrix, where the entries are blank for stationswhere signal is not received and/or when received signals are below somethreshold value.

In numerous embodiments, the captured signal information is noisy data(e.g., raw RSSI values) that may need to be cleaned. Cleaning noisy datain accordance with various embodiments can include computing a weightedaverage of historic RSSI values for each signal path to reduce some ofthe high-frequency noise common in RSSI values. In a number ofembodiments, the weighting factor can be based on timestamps of eachRSSI value (e.g., weighting weight recent RSSI values more heavily andreducing the weight of older RSSI values). Timestamps in accordance withvarious embodiments can include timestamps for when a signal is detectedat a receiver and/or transmitted from a sender.

Process 700 normalizes (715) the measured signal characteristics toestimate signal path characteristics. In many embodiments, normalizingthe data can help to account for differences in the constructions of theWI-FI radios and front-end circuitries of each device based on theassumption that RSSI values associated with a signal transmitted frompoint A to point B should be approximately equal to the RSSI valuesassociated with the same signal being transmitted in the oppositedirection from point B to point A. Normalizing signal characteristics inaccordance with some embodiments can include calculating an average ofthe sent and received signals of a signal path between two devices(e.g., a portable device and a reference device, and/or between tworeference devices). Processes in accordance with numerous embodimentscan compare RSSI values associated with each pair of signal paths (i.e.,paths to and from another reference device) to identify an offset inRSSI values. In certain embodiments, identified offsets in RSSI valuescan be used as a basis to normalize the values to account for thedifferences in the construction of the radios.

An example of normalizing signal strengths between two devices isillustrated in FIG. 9 . The first chart 905 illustrates both themeasured RSSI values associated with a transmission from a first device(e.g., a Sonos Beam) to a different, second device (e.g., a Sonos One)as well as the RSSI values associated with a transmission from thesecond device back to the first device. In this example, the measuredsignals in the two directions between the devices are different, eventhough the signal path length between the devices remains the same. Thesecond chart 910 illustrates a graph of the difference between therecorded signals.

Process 700 estimates (720) a likelihood that a portable device is in aparticular location based on the estimated signal path characteristics.For example, processes in accordance with various embodiments cancompute a probability that a given player and/or smartphone executingthe Sonos app is at/near a particular location (e.g., near a particularstationary Sonos player). This computation may be performed by a Sonosplayer in the system and/or the controller on the smartphone. Forexample, a single player may aggregate all of the information (e.g.,RSSI values) for the computation, perform the computation, and providethe result (e.g., to the Sonos controller app). In certain embodiments,a machine learning model is trained to estimate the likelihoods based onsignal information as input that is labeled with a nearest referencedevice.

From an intuitive standpoint, the stronger the RSSI values associatedwith a given signal path, the shorter the length of the signal path. Forexample, if the RSSI values associated with the signal path from aroaming device to a player P1 are high, the roaming device is likelynear player P1.

Because RSSI values can be obtained for a large number of signal paths,processes in accordance with a variety of embodiments can layer onadditional logic to confirm that the roaming device is actually near agiven device. In this example, if the roaming device is actually quiteclose to P1, the RSSI values associated with the signal path from theroaming device to a second player P2 should be substantially similar tothe values associated with the signal path from the first player P1 toP2. Similarly, if the roaming device is actually quite close to P1, theRSSI values for the path from the remote device to a third device P3should be substantially similar to the RSSI values for the path from P1to P3. Accordingly, processes in accordance with numerous embodimentscan analyze RSSI values associated with multiple different signal pathsin an MPS to come up with a probability that a roaming device is near agiven stationary device.

Processes in accordance with numerous embodiments can estimatelikelihoods based on a physical/probabilistic model. In severalembodiments, signal attenuation can be used as a basis, but can bemodified to feed a probabilistic model. Processes in accordance withvarious embodiments can use the entire system (or a subset of a system)as the distance metric for total attenuation (A) since the decayconstant (T) and Euclidean distance (D) are entangled within the system.S _(AB) =T _(A) R _(B) A _(AB)A _(AB) =e ^(−D) ^(AB) ^(/τ)where T is transmission, R is reception fraction, A is totalattenuation, S is recorded signal, D is Euclidean distance, and τ is adecay constant. The total attenuation (A) is assumed identical along asame path (i.e., in both directions along the path). The notationidentifies directionality of a transmission:S _((Transmitter)(Receiver)):RSSI between devicesIn the case of two reference devices, the probability P_(A) that theportable device is nearest to a device A, can be computed as:

$P_{A} \propto \frac{A_{AB}}{A_{BC}} \propto \frac{S_{CA}/S_{BA}}{S_{CB}/S_{AB}}$The probability P_(B) that the portable device is nearest to device B,can be computed as:P _(B)∝(1−P _(A))

When there are more than three devices, physical/probabilistic models inaccordance with various embodiments can provide a baseline that providesa more robust probability of the likely closest device. More generally,in numerous embodiments, the probability that a portable device isnearest to a given device F can be calculated as:

$P_{F} \propto {\frac{S_{MF}}{S_{MR}}\frac{S_{FR}}{S_{RF}}}$where M is the moving device, F is a fixed station, and R is a referencestation. The probability for the reference station R can then becalculated as:

$P_{j} \propto {\sum\limits_{{i = 1},{i \neq j}}^{n}\frac{1 - P_{i}}{n - 1}}$

In numerous embodiments, the likelihood that a portable device isnearest to a given device changes as the device moves around. An exampleof changing likelihoods based on movement of a portable device isillustrated in FIG. 10 . The first chart 1005 shows the calculatedprobabilities for each of three reference devices (SB3, SB4, and SB5)and how they change over a period of time. The second chart 1010 showsthe changes in the nearest device, based on the calculatedprobabilities, over the same period of time.

In certain embodiments, the estimated locations of portable devices canbe used in a variety of applications, such as (but not limited to)controlling a nearest reference device, presenting an ordered list ofnearest reference devices to a GUI of the portable device, monitoringmovement through a household, etc. Estimated locations in accordancewith numerous embodiments can be used as inputs to another predictionmodel that can be used to identify a target reference device based onthe location information. In various embodiments, a probability matrixfor reference devices in an MPS can be used as an input to a machinelearning model that can predict a target device (or a device a userwishes to interact with). The resulting matrix in accordance with manyembodiments is an n×n matrix of probabilities that can be used to definea robust metric for identifying room localization. Predicting targetdevices is discussed in greater detail below.

While specific processes for localizing a portable device are describedabove, any of a variety of processes can be utilized as appropriate tothe requirements of specific applications. In certain embodiments, stepsmay be executed or performed in any order or sequence not limited to theorder and sequence shown and described. In a number of embodiments, someof the above steps may be executed or performed substantiallysimultaneously where appropriate or in parallel to reduce latency andprocessing times. In some embodiments, one or more of the above stepsmay be omitted.

An example of localizing a portable device in a networked sensor systemin accordance with an embodiment is illustrated in four stages 1105-1120of FIG. 11 . The stages of this example show a networked system with aportable device 1125, and reference devices 1140-1144. In the firststage, reference devices 1140-1144 transmit and receive signals amongeach other. Signals in accordance with some embodiments are part of awireless scan that is periodically performed by each reference device tomaintain information regarding the other reference devices in an MPS.

In the second stage 1110, portable device 1125 transmits signals toreference devices 1140-1144. In this example, the portable devicetransmits to the reference devices, but portable devices in accordancewith a number of embodiments can receive signals transmitted by thereference devices. Signal characteristics and/or statistics can begathered and transmitted to a coordinator device for further processing.

In the third stage 1115, reference device 1140 has been selected as acoordinator device. Coordinator devices in accordance with numerousembodiments are designated to collect and process signal informationfrom an MPS, and can be selected from the available devices of an MPSbased on one or more of several factors, including (but not limited to)RSSI of signals received from the portable device at the referencedevices, frequency of use, etc. The third stage 1115 shows thatreference devices 1142 and 1144 send their signal information tocoordinator device 1140. In many embodiments, coordinator devices can beplayback devices, portable devices, controller devices, etc. Forexample, in some embodiments, once the individual devices have collectedthe proper signals from each other, the individual devices can send thesignal information to the portable coordinator device, which can thenprocess the signal information to locate the portable device. Processingin accordance with several embodiments can include (but is not limitedto) cleaning the raw signal data, calculating statistics, normalizingRSSI values, and/or passing the signal information through a machinelearning model to determine a nearest reference device.

In the fourth stage 1120, coordinator device 1140 communicates back withreference device 1144, which was identified as the nearest device (orgroup of devices). While in this example coordinator device 1140communicates with only the target reference device, coordinator devicesin accordance with numerous embodiments can communicate with one or moreof the nearest devices, one or more portable devices, and/or anycombination thereof without departing from the scope of the presentdisclosure. In many embodiments, coordinator devices can communicatevarious information to the devices, such as (but not limited to)playback controls, recommended content, a sorted lists of targetdevices, etc.

As can readily be appreciated the specific system used to localize amobile device is largely dependent upon the requirements of a givenapplication and should not be considered as limited to any specificcomputing system(s) implementation.

a. Localization Element

An example of a localization element that can execute instructions toperform processes that locate portable devices in a networked devicesystem (e.g., an MPS) in accordance with various embodiments is shown inFIG. 12 . Localization elements in accordance with many embodiments caninclude various networked devices, such as (but not limited to) one ormore of portable devices, stationary playback devices, wirelessspeakers, Internet of Things (IoT) devices, cloud services, servers,and/or personal computers. In this example, localization element 1200includes processor 1205, peripherals 1210, network interface 1215, andmemory 1220. One skilled in the art will recognize that a particularlocalization element may include other components that are omitted forbrevity without departing from the scope of the present disclosure.

The processor 1205 can include (but is not limited to) a processor,microprocessor, controller, or a combination of processors,microprocessor, and/or controllers that performs instructions stored inthe memory 1220 to manipulate data stored in the memory. Processorinstructions can configure the processor 1205 to perform processes inaccordance with certain embodiments.

Peripherals 1210 can include any of a variety of components forcapturing data, such as (but not limited to) cameras, displays, and/orsensors. In a variety of embodiments, peripherals can be used to gatherinputs and/or provide outputs. Network interface 1215 allowslocalization element 1200 to transmit and receive data over a networkbased upon the instructions performed by processor 1205. Peripheralsand/or network interfaces in accordance with many embodiments can beused to gather inputs (e.g., signals, user inputs, and/or contextinformation) that can be used to localize portable devices.

Memory 1220 includes a localization application 1225, signal data 1230,and model data 1235. Localization applications in accordance withseveral embodiments can be used to localize portable devices in anetworked system of devices. In numerous embodiments, signal data caninclude data captured at the localization element. Signal data inaccordance with a number of embodiments can include signal informationreceived from other reference devices and/or portable devices. Modeldata in accordance with some embodiments can include parameters for amachine learning model trained to generate probabilistic locationinformation based on input signal characteristics.

Although a specific example of a localization element 1200 isillustrated in FIG. 12 , any of a variety of such elements can beutilized to perform processes similar to those described herein asappropriate to the requirements of specific applications in accordancewith embodiments.

b. Localization Application

FIG. 13 illustrates an example of a localization application inaccordance with an embodiment. Localization applications in accordancewith a variety of embodiments can be used for locating portable devicesin a networked system. In this example, the localization applicationincludes transmission engine 1305, receiver engine 1310, data collectionengine 1315, normalizing engine 1320, likelihood engine 1325, and outputengine 1330. As can readily be appreciated, localization applicationscan be implemented using any of a variety of configurations appropriateto the requirements of specific applications.

Transmission engines and receiver engines in accordance with a varietyof embodiments can be used to transmit and receive signals betweenreference devices and/or portable devices. In many embodiments,transmission engines can broadcast wireless signals as part of aperiodic wireless scan. Receiver engines in accordance with certainembodiments can receive signals broadcast by other reference devicesand/or the portable device.

Data collection engine 1315 collects signal data from the other devicesof the MPS. In many embodiments, data collection engines can alsoperform some pre-processing and/or cleaning of the signal data. In anumber of embodiments, pre-processing and/or cleaning are distributedbetween a coordinator device and one or more reference devices. Datacollection engines in accordance with a number of embodiments canmaintain a history of signal data in order to compute statistics (e.g.,weighted averages) of the historic signal data.

Once the data has been collected, normalizing engine 1320 can normalizethe collected data. Normalizing signal characteristics in accordancewith some embodiments can include calculating an average of the sent andreceived signals of a signal path between two devices (e.g., a portabledevice and a reference device, and/or between two reference devices).

Likelihood engines in accordance with a number of embodiments cancompute the likelihood that a portable device is nearest to a particularreference device of an MPS. In various embodiments, likelihood enginescan include a physical/probabilistic model to estimate likelihoods basedon signal strengths for signals received from reference devices and theportable device in relation to each other. Likelihood engines inaccordance with a variety of embodiments can calculate signal strengthratios for signals received at various devices in an MPS, where theprobability that the portable device is nearest to a particularreference device is a ratio between a first signal ratio for signalsreceived at the particular reference device and a second signal ratiofor signals received at a second reference device.

In a variety of embodiments, output engines can provide outputs to adisplay and/or transmit instructions and/or information to devices in anMPS based on the computed likelihoods. Outputs in accordance with someembodiments can include but are not limited notifications, alerts,playback controls, ordered device listings, etc. In various embodiments,outputs can include a probability matrix that can be used as an input toa prediction model. Prediction models in accordance with numerousembodiments are discussed in greater detail below.

Although a specific example of a localization application 1300 isillustrated in FIG. 13 , any of a variety of localization applicationscan be utilized to perform processes for localizing portable devicessimilar to those described herein as appropriate to the requirements ofspecific applications in accordance with embodiments. In someembodiments, one or more of the above elements may be omitted and/oradditional elements may be added.

IV. Playback Device Management

In some cases, beyond (or in addition to) localizing a portable device,systems and methods in accordance with many embodiments can be used todetermine a layout or state of a system, such as (but not limited to) ahome theater (HT) system and an MPS. In several embodiments, playbackdevices of a HT system can include portable satellite speaker devicesthat can be moved and used in other contexts (e.g., for travel, atanother location in the home, etc.). When returning the playback devicesto their original positions, the positions of the satellite speakers canbe different from the original placement. Processes in accordance withsome embodiments can be used to detect when the layout (e.g., positionand/or orientation) of the satellite speakers has changed and can reactaccordingly, e.g., by modifying parameters for the speakers and/orproviding notifications to a user regarding the misplaced speakers.

In some examples, the original layout of the satellite speakers can bedetermined through a calibration process for optimizing the soundexperience. Processes in accordance with a variety of embodiments candetermine a difference between a current layout and the original layoutand can recommend recalibrating the system when the difference exceeds agiven threshold. When the difference does not exceed the threshold,processes in accordance with some embodiments can provide instructionsto modify the layout and/or modify parameters of the devices to accountfor the changed layout.

In some cases, the layout of the devices does not change, but rather thestate of the space between the devices changes. For example, individualsmay enter the space, move between different positions in the space,rearrange furniture in the space, etc. In several embodiments, changesin the space state can be determined based on signal patterns measuredbetween devices of the space. Detected changes in space state can beused to modify devices of the system (e.g., modifying playbackparameters, configuration, etc.).

An example of a process for playback device management in accordancewith an embodiment is conceptually illustrated in FIG. 14 . Process 1400measures (1405) a first signal pattern for wireless signals betweenmultiple devices. In certain embodiments, the first signal pattern is anaggregate measure (e.g., average, median, etc.) of signals betweenmultiple devices. Signal patterns in accordance with numerousembodiments can include measures of various signals between multipledevices, such as (but not limited to) received signal strength indicator(RSSI), signal direction, etc.

In several embodiments, first signal patterns can include patternsmeasured from an original layout prior to changes to the system.Original layouts in accordance with various embodiments can includelayouts that are determined upon performing a calibration process.Calibrated layouts can be set up to place and/or orient speakers in aspace to optimize sound quality. First signal patterns in accordancewith a number of embodiments can include baseline patterns that indicatea baseline state of a system. In several embodiments, baseline patternscan be measured during a period of inactivity. Inactivity in accordancewith numerous embodiments can be determined in a variety of ways,including (but not limited to) after a user has not interacted with anyelements of the system for a threshold period of time, at particulartimes of day (e.g., during the middle of the night) when the system isnot expected to be in use (e.g., based on historic system usagepatterns), based on sensor measurements (e.g., video, motion sensors,etc.) that indicate that there are no people in the area of a system,etc.

Process 1400 measures (1410) a second signal pattern for wirelesssignals, after measuring the first signal pattern, between the multipledevices. In numerous embodiments, second signal patterns can be measuredperiodically, based on user input, and/or based on other indications ofchanges in the system. Processes in accordance with many embodiments cantrigger measurements of a second signal pattern when a playback devicereconnects to a home network.

Process 1400 determines (1415) an updated state of the playback systembased on a difference between the first and second signal patterns.Differences between the first and second signal patterns can be used toindicate changes in the state of a playback system. Changes in the stateof a playback system can include (but are not limited to) an updatedlayout (e.g., orientation and/or placement of playback devices) and/orspace state (e.g., count and/or location of people and/or other objectsin the space between devices). In some embodiments, quantitative metricscan be used to measure the degree of change in a system between eachwindow of time when signal patterns are measured. The signal reportedfor each time (or time window) can be recorded as a statisticaldistribution around a mean value for each signal strength measurementfrom device to device. When comparing signal patterns, processes inaccordance with several embodiments of the invention can determine aquantitative distance metric by comparing each measurement to itsequivalent measure at a later time through a number of differentestablished methods, such as (but not limited to) Euclidean distance,Manhattan distance, cosine similarity metric, etc.

Process 1400 modifies (1420) the playback system based on the determinedupdated state. Modifying the playback system in accordance with avariety of embodiments can include, but is not limited to, modifyingstate variables of one or more playback devices. State variables caninclude (but are not limited to) which channel is being input to theplayback device, equalizer settings, volume, microphone sensitivities,etc. In a variety of embodiments, processes can determine when a layouthas changed from an original calibrated layout based on measured signalpatterns, and, when the change exceeds a threshold, can recommendrecalibration of the system. In numerous embodiments, a singlecoordinator device can determine settings for each playback device in asystem based on the measured signal patterns. Alternatively, orconjunctively, each playback device can use the measured signal patternsto determine an appropriate modification to its own settings.

While specific processes for managing playback devices are describedabove, any of a variety of processes can be utilized as appropriate tothe requirements of specific applications. In certain embodiments, stepsmay be executed or performed in any order or sequence not limited to theorder and sequence shown and described. In a number of embodiments, someof the above steps may be executed or performed substantiallysimultaneously where appropriate or in parallel to reduce latency andprocessing times. In some embodiments, one or more of the above stepsmay be omitted. Although the above embodiments are described inreference to home theater systems, the techniques disclosed herein maybe used in any type of wireless device system, including (but notlimited to) an MPS, a network of Internet of Things (IoT) devices, etc.

a. Layout Element

An example of a layout element for determining the layout of a system inaccordance with an embodiment is illustrated in FIG. 15 . Layoutelements in accordance with many embodiments can include (but are notlimited to) one or more of mobile devices, playback devices, homerouters, controller devices, and/or other computing devices. Layoutelement 1500 includes processor 1505, peripherals 1510, networkinterface 1515, and memory 1520. One skilled in the art will recognizethat a particular layout element may include other components that areomitted for brevity without departing from this invention.

The processor 1505 can include (but is not limited to) a processor,microprocessor, controller, or a combination of processors,microprocessor, and/or controllers that performs instructions stored inthe memory 1520 to manipulate data stored in the memory. Processorinstructions can configure the processor 1505 to perform processes inaccordance with certain embodiments.

Peripherals 1510 can include any of a variety of components forcapturing data, such as (but not limited to) cameras, motion detectors,microphones, displays, transceivers, and/or sensors. In a variety ofembodiments, peripherals can be used to gather inputs and/or provideoutputs. Network interface 1515 allows layout element 1500 to transmitand receive data over a network based upon the instructions performed byprocessor 1505. In numerous embodiments, network interfaces can be usedto exchange signal patterns to allow a device to analyze signal patternsof multiple devices in the system. Peripherals and/or network interfacesin accordance with many embodiments can be used to gather inputs thatcan be used to determine signal patterns and/or to update a system basedon a determined layout.

Memory 1520 includes a layout application 1525, pattern data 1530, andmodel data 1535. Layout applications in accordance with severalembodiments can be used to determine layouts of a system and/or update asystem based on a determined layout. In a number of embodiments, layoutapplications can include playback system management software that canupdate settings for various playback speaker devices in a home theatersystem. An example of a layout application in accordance with anembodiment is described with reference to FIG. 16 .

Pattern data in accordance with a variety of embodiments can includevarious patterns of signals between devices of a system. In severalembodiments, pattern data can include RSSI of signals between differentwireless devices. Pattern data in accordance with many embodiments caninclude baseline measurements that are measured to determine a baselinestate of signals in the system as well as other state measurements thatcan be used to determine changes in the state (e.g., playback devicelayout and/or space state). In several embodiments, pattern data caninclude pattern data for a calibrated layout that can be used todetermine whether a system needs to be recalibrated.

In several embodiments, model data can store various parameters and/orweights for models. Models in accordance with certain embodiments can betrained to perform various processes based on pattern data, including(but not limited to) predict target actions and/or states, classify acurrent state of the system, etc. Model data in accordance with manyembodiments can be updated through training on pattern data captured onthe layout element and/or can be trained remotely and updated at thelayout element. In various embodiments, model data can include data formultiple models that can be used to determine how to update a system.For example, a first model can be used to determine a space state orlayout of the system based on measured signals and a second model canuse the determined state of the system to predict a target action and/ordevice.

Although a specific example of a layout element 1500 is illustrated inFIG. 15 , any of a variety of layout elements can be utilized to performprocesses for determining layouts of a system and/or updating a systembased on a determined layout similar to those described herein asappropriate to the requirements of specific applications in accordancewith embodiments.

b. Layout Application

An example of a layout application for determining layouts of a systemand/or updating a system based on a determined layout in accordance withan embodiment is illustrated in FIG. 16 . Layout application 1600includes transmission engine 1605, receiver engine 1610, data collectionengine 1615, pattern engine 1620, and output engine 1625.

Transmission engines and receiver engines in accordance with a varietyof embodiments can be used to transmit and receive signals betweendevices of a system. In many embodiments, transmission engines canbroadcast wireless signals as part of a periodic wireless scan. Receiverengines in accordance with certain embodiments can receive signalsbroadcast by other reference devices and/or the portable device.

Data collection engines can collect signal data from the other devicesof the MPS. In many embodiments, data collection engines can alsoperform some pre-processing and/or cleaning of the signal data. In anumber of embodiments, pre-processing and/or cleaning are distributedbetween a coordinator device and one or more reference devices. Datacollection engines in accordance with a number of embodiments canmaintain a history of signal data in order to compute statistics (e.g.,weighted averages) of the historic signal data.

Pattern engines in accordance with various embodiments can be used torecord and/or compare signal patterns in a system. In many embodiments,pattern engines can compare a signal pattern to a baseline signalpattern (and/or a signal pattern of a calibrated layout) to determine astate of a system. Comparisons between signal patterns in accordancewith certain embodiments can include subtracting a baseline signal froma pattern to identify changes in the system.

Output engines in accordance with several embodiments can provide avariety of outputs, including (but not limited to) notifications to auser to modify a system (e.g., physically reposition, recalibrate,and/or reorient a playback device), control signals to playback devicesto update settings of the playback device, etc.

Although a specific example of a layout application 1600 is illustratedin FIG. 16 , any of a variety of layout applications can be utilized toperform processes for determining layouts of a system and/or updating asystem based on a determined layout similar to those described herein asappropriate to the requirements of specific applications in accordancewith embodiments.

c. System Layout

An example of determining a layout of a system with a reference devicein accordance with an embodiment is illustrated in four stages in FIG.17 . The first stage 1705 shows a soundbar 1740, subwoofer 1745, andsatellite devices 1750 and 1755. In this example, device 1750 isconfigured as the left speaker, while device 1755 is configured as theright speaker in a stereo system. In the first stage 1705, signalcharacteristics between the various devices of the system are measuredto determine a first signal pattern. In particular, signals are measuredbetween the satellite devices 1750 and 1755 and reference devices 1740and 1745. Reference devices in accordance with a number of embodimentscan include various elements of a system, such as (but not limited to) asoundbar, a subwoofer, a home router, other connected devices, etc. Inthis example, soundbar 1740 is positioned between the satellite devices,while subwoofer 1745 is offset from the center of the satellite devices.In many embodiments, reference devices can introduce asymmetry which canvalidate the variation in positioning. In certain embodiments, initialpositions for each reference device and/or playback device can beidentified via the distribution of proximity probabilities during setup.Processes for determining proximity probabilities in accordance withseveral embodiments are described throughout this description.

In the second stage 1710, speakers 1750 and 1755 have switchedpositions, although they are still configured as right and leftspeakers, respectively. When surround devices are removed then placed inswapped positions, the measurement of received signals can identify thatthe positions have been swapped. In a number of embodiments, processescan periodically scan the signal patterns between the devices andcompare the scanned patterns with a baseline pattern to determine thatthe layout of the system has changed (e.g., when a difference exceeds agiven threshold).

The third stage 1715 shows that signal patterns between the variousdevices of the system are measured to determine a second signal pattern.The second signal pattern can be compared to the first signal pattern todetermine whether the layout has changed. In certain embodiments, thesignal patterns can show that a device that was closer (i.e., hadstronger signals) to a reference device than another device in the firstsignal pattern, has moved to be farther from the reference device in thesecond signal pattern, indicating that their positions have beenswitched.

An example of signal strength patterns measured with multiple referencedevices in accordance with an embodiment illustrated in FIG. 18 . Inthis example, chart 1805 shows signal strengths measured between tworeference devices (HT and SUB) and two satellite speakers (S1 and S2)while in a first position (e.g., a baseline position with speaker 1 onthe left and speaker 2 on the right). Chart 1810 shows signal strengthsmeasured between the reference devices HT and SUB and the satellitespeakers S1 and S2 after the speakers have been moved to a differentposition (e.g., when the right speaker has switched positions with theleft speaker). As shown in these charts, the relative strengths for thepaths between each satellite speaker and the reference devices changewith the different positions. In this example, the pattern for speakerS2 in the first position 1805 is similar to the pattern for speaker S1in the second position 1810 because their positions have been swapped.

In the fourth stage 1720, satellite device 1755 has been reconfigured asthe left speaker and satellite device 1750 has been reconfigured as theright speaker. In several embodiments, rather than reconfiguringspeakers, processes can provide instructions to a user to repositionand/or reorient devices to return the devices of the system to abaseline configuration. Processes in accordance with a number ofembodiments can determine whether to provide instructions, reconfigurethe speakers, and/or recalibrate the system as a whole based on howclose the system is to a baseline configuration.

In some cases, rather than using an offset reference device, processesin accordance with numerous embodiments can determine the layout of asystem using multiple radio chains on a single device. An example ofdetermining a layout of a system with multiple radio chain devices inaccordance with an embodiment is illustrated in four stages in FIG. 19 .Devices in accordance with some embodiments can include multiple radiosfor multiple-input and multiple-output (MIMO) communication. Once asignal pattern for a position and/or orientation has been established,the relation of signal strengths can identify the position and/ororientation of a device.

In the first stage 1905, signals are measured between soundbar 1940 andsatellite devices 1950 and 1955, similar to the example of FIG. 17 .However, in this example, rather than using an offset reference device,signals are measured between each of multiple radio chains of eachsatellite device. Satellite devices 1950 and 1955 each have four radiochains (indicated as circles, where the front-facing radio chain foreach satellite device is filled-in). In this example, the longer arrows(indicating longer paths and weaker signals) are pointed at the oppositeouter antennas, while the shorter arrows (indicating shorter paths andstronger signals) are pointed at the inner antennas.

In a number of embodiments, rather than exchanging signals betweendevices, signal measurements can be measured in only one direction(e.g., with only the soundbar device to provide signal). By exchangingsignals in both directions between devices, processes in accordance withseveral embodiments of the invention can add confidence to theidentification of position, because multiple measurements (i.e., sendingand transmitting) can be used for each exterior source. In certainembodiments, the multiple measurements can be aggregated (e.g.,averaged) to determine a normalized signal strength between two devices.

In the second stage 1910 of this example, the two satellite playbackdevices 1950 and 1955 are swapped in position. This can occur in hometheater systems with portable multi-purpose speakers, where the speakerscan be used as portable speakers in a different setting before beingreturned to the home theater system.

The third stage 1915 shows that signals are again measured betweendevices of the system 1900. Since speakers 1955 and 1950 are facing thesame direction, their positions relative to soundbar 1940 can bediscerned based on the changes in the relative strengths of the otherantennas. From the point of view of the right surround speaker 1955, theradio strength went from strong on the left antenna to strong on theright antenna. For the left surround speaker 1950, the change was theopposite. In a variety of embodiments, signal strength patterns fordevices with multiple radio chains can be used to determine deviceorientation and/or position. By determining which radio chains havestronger signals than before and which radio chains have weaker signals,processes in accordance with a variety of embodiments can determine theorientation and/or position of the device. In a variety of embodiments,rather than comparing the actual signal strengths, processes can comparethe relative strengths (e.g., whether a signal to a first radio chain isstronger than the signal to a second antenna).

An example of signal strength patterns measured in accordance with anembodiment illustrated in FIG. 20 . In this example, charts 2005 and2010 show signal strengths measured between each of four radio chains ofspeakers 1 and 2 while in a first position (e.g., a baseline positionwith speaker 1 on the left and speaker 2 on the right). Charts 2015 and2020 show signal strengths measured between the radio chains of speakers1 and 2 after the speakers have been moved to a different position(e.g., when the right speaker has switched positions with the leftspeaker). As shown in these charts, the relative strengths of thedifferent radio chains change with the different positions, such thatthe relative strengths of the radio chains when speaker 1 is on theleft, are similar to the relative strengths of corresponding radiochains of speaker 2 when it is on the left.

In the fourth stage 1920, satellite devices 1950 and 1955 have beenrepositioned and reoriented to their original positions. In severalembodiments, processes can provide instructions to a user to repositionand/or reorient devices to return the devices of the system to abaseline configuration. In various embodiments, processes can modifysettings (e.g., channel settings, equalizer values, volume, etc.) of theplayback devices and/or other playback devices in the system to returnthe system to a baseline configuration. Processes in accordance with anumber of embodiments can determine whether to provide instructions,reconfigure the speakers, and/or recalibrate the system as a whole basedon how close the system is to a baseline configuration.

Although many of the examples described herein describe swappedpositions of satellite speakers, one skilled in the art will recognizethat similar systems and methods can be used in a variety ofapplications, including (but not limited to) the detection of changes inrelative orientation, as well as the positioning and/or orientation ofother types of wireless devices, such as (but not limited to) Internetof Things (IoT) devices, routers, standalone speakers, etc. withoutdeparting from this invention.

d. Space State

Rather than determining the layout of devices relative to each other,systems and methods in accordance with some embodiments can measuresignals to determine a space state. Space states in accordance with manyembodiments can measure characteristics of the space between the devicesof an MPS, such as (but not limited to) a number of people in the space,positions of people and/or objects in the space, pattern. In numerousembodiments, space state can be used to determine parameters forplayback devices of a HT system. For example, volume settings forsatellite speakers can be adjusted based on a determined location of auser in the space.

An example of determining a space state in accordance with an embodimentis illustrated in four stages in FIG. 21 . The first stage 2105 shows ahome theater system 2100 with a soundbar 2144 and satellite playbackdevices 2150 and 2155. In the first stage, only a couch is in the spacebetween the various devices. Signals between the devices are measuredand captured as a baseline measurement. By measuring signal strengthamong devices when there is no one in the area (e.g., in the middle ofthe night, when no one is detected in the area, etc.), processes inaccordance with several embodiments can capture a baseline measurementof the overall system signal pattern. In certain embodiments, baselinemeasurements can be subtracted off of the signal strength measurementsto show patterns in signal strength consistent with the location of aperson without the need for a controller or other sensor for detectingthe location of an individual. Subtracting off the baseline inaccordance with certain embodiments of the invention can make changes insignal patterns more evident. Alternatively, or conjunctively, processesin accordance with some embodiments of the invention can applynormalizations to the measured signal patterns to ensure that the scaleof the pattern is consistent. Such adjustments can help because thepertinent parameter is the change in signal over the system, which canbe small in magnitude, rather than the full-scale signal strengthmeasurement. Additionally, the baseline itself can change slowly assmall factors in the environment change, so by tracking the baseline,the introduction of interference by such factors can be more readilydetected.

The second stage 2110 shows that a person has taken a seat on the couch.In the third stage 2115, signals are again measured between soundbar2144 and satellite playback devices 2150 and 2155. Recorded signalpatterns can then be compared to the baseline signal pattern todetermine that the space state has changed.

In a number of embodiments, measured signal patterns can be comparedwith calibration signal patterns that are recorded during a calibrationsession, where calibration signal patterns can be measured andassociated with a “true” location for an individual. True locations inaccordance with several embodiments can be determined through variouslocalization techniques such as (but not limited to) those describedthroughout this application, via external sensor systems (e.g., cameras,motion sensors, etc.), and/or manual location information received viauser inputs.

In a number of embodiments, recorded signal patterns can be used todetermine a location of a user in space. Example charts of signalpatterns for a person in different positions is illustrated in FIG. 22 .This example shows signal patterns from two trials, on the left andright side. For each trial, there are three charts for when a person isseated in a left seat (2205 and 2220), in a middle seat (2210 and 2225),and in a right seat (2215 and 2230) between satellite playback devices(L and R). As can be seen in this example, the signal patterns of thedifferent trials are similar to each other, based on the location of theperson in the space between the playback devices. Although many of theexamples described herein show three or four devices in a playbacksystem, one skilled in the art will recognize that similar systems andmethods can be used in a variety of applications with different numbersof devices, including (but not limited to) localizing a user at homewithout a portable device based on signal patterns of various devices ofan MPS, without departing from this invention.

In many embodiments, processes do not identify specific positions ofpeople within the space, but rather use the signal patterns measuredbetween the playback devices as inputs to a model. In many embodiments,models (e.g., deep neural networks) can then be trained to predictdesired actions, settings, configurations, and/or other outputs that canbe performed based on the signal patterns.

In the fourth stage 2120, soundbar 2140 sends control signals to thesatellite playback devices 2150 and 2155 to modify settings of thespeakers based on the detected signal pattern. Settings can include (butare not limited to) volume, equalizer settings, balance, fade,microphone sensitivity, etc.

In many embodiments, signal strength patterns for a given configurationcan remain similar at different scales. Example charts of signalstrength patterns with different surround positions are illustrated inFIG. 23 . In this example, the surround position is increased with eacharrangement from left to right. While the overall system mean reduceswith an increase in position, the signal patterns maintain a similarrelative pattern. The variance among the system signal strength valuesdecreases as well.

V. User Interaction Prediction

Systems and methods in accordance with several embodiments can be usedto train prediction models and/or to predict target devices usingtrained prediction models. For example, a coordinator device inaccordance with various embodiments can predict a stationary playbackdevice that a user would want to interact with next (which may or maynot be the player closest to them) using a machine learning model thatlearns over time.

Processes in accordance with numerous embodiments can build and train amachine learning model that can receive signal characteristics (e.g.,raw or pre-processed RSSI values) as input and generate, based on thosesignal characteristics, an indication of the reference device (e.g., astationary player) that a person is standing near. In certainembodiments, the information that the person is standing near aparticular device can be used to drive new user experiences (e.g.,highlighting the speakers in the Sonos app a user is standing closestto, shifting music to play on different speakers as a user walks througha space). However, in some cases, a user may want to control a speakerother than the one to which they are closest. For example, the user mayalways turn on the kitchen Sonos speakers first thing in the morningfrom their bedroom because that is the room they intend to go to next.As a result, the underlying assumption that a user wants to alwaysinteract with the closest player doesn't hold in many situations.

In some embodiments, user interaction information can be leveraged toidentify these types of unique user behaviors. In many embodiments, newtraining data can be constructed from these types of unique userbehaviors that can be used to train (and/or retrain) a machine learningmodel to identify the desired interactions. In numerous embodiments, apredictive machine model is initially trained to identify the closeststationary speaker, and is then retrained with new training data that isspecific to the user and/or household. In certain embodiments, thepredictive machine model is initially trained using a singular valuedecomposition of the probability matrix as input and an online trainingprocess is used as more data is collected to build a more sophisticatedmodel. Predictive machine learning models in accordance with manyembodiments of the invention can take one or more signal patterns from anumber of devices in the system as input to provide space state ascontext for a predicted action. In many embodiments, various localonline models can be used as interactions are refined; such that datacan remain private and does not need to be sent out to remote computingdevices. Thus, the predictive model can start to take into account theparticular patterns of the user. As a result, predictive machinelearning models in accordance with various embodiments can adapt overtime to each particular user so that the “best guess” of the device theywant to interact with next may get better over time.

Predicting a target device (or user interaction) in accordance withvarious embodiments can be used to simplify controllers in an MPS. Forexample, upon detecting activation of a “Play/Pause” button in the SonosApp, the Play/Pause command can be automatically applied only to thoseplayers that are proximate the Wuser's smartphone. In another example,the particular player (or zone group) that an individual is standingnear can be emphasized (e.g., highlighted, made bold, etc.) in the SonosApp to help the individual to control the correct zone group (instead ofanother zone group).

In a number of embodiments, predicted target devices can be used to swapcontent between devices. For example, processes in accordance with manyembodiments can allow a user wearing headphones to transition (e.g., viaa gesture on the capacitive touch sensor on the headphones) music frombeing played on the headphones to being played on nearby speakers.

An example of a process for training a prediction model in accordancewith an embodiment is conceptually illustrated in FIG. 24 . In a numberof embodiments, prediction models operate and/or are re-trained locallyat one of the devices in an MPS, allowing the prediction models tocontinually adjust to a user's interactions.

Process 2400 monitors (2405) an MPS. In a variety of embodiments,monitoring an MPS can include monitoring (but is not limited to) thelocation (or trajectory) of one or more portable devices within an MPS,signal patterns between devices of an MPS, user interactions withdevices (e.g., portable, stationary, playback, controllers, etc.) of theMPS, and/or predictions of user interactions by a prediction model ofthe MPS.

Process 2400 determines (2410) whether to capture a training data samplebased on the monitoring of the MPS. The determination of whether tocapture training data can be based on a number of different criteria.For example, in certain embodiments, processes can determine to capturea training data sample when a confidence level for a prediction for thelocation of a portable device is below a particular threshold. Therewill likely be instances where the output of a prediction model isfluctuating between two states or otherwise has a low confidence answer.In these scenarios, processes in accordance with various embodiments candetermine to capture training data samples to identify a user's trueinteractions in order to identify a correct answer. For example,processes in accordance with a number of embodiments can identify thenext speaker that the user interacts with as the “correct” answer andpair that correct answer with the RSSI values during that low-confidencescenario to form a new training data point.

Processes in accordance with many embodiments can determine whether tocapture a training data sample based on a user's actual (and/orpredicted) interactions (e.g., initiating playback, changing thecurrently playing content, voice commands, physical interactions, etc.)with a device in an MPS. In certain embodiments, training data samplesare captured when a predicted user interaction does not match with theuser's actual interaction. For example, the prediction model may predict“living room speaker,” but the speaker that a user actually controlsusing the Sonos app is “bedroom.”

When the process determines (2410) not to capture training data, theprocess returns to step 2405 and continues to monitor the MPS. When theprocess determines (2410) to capture training data, the process gathers(2415) context data. In numerous embodiments, context data can provideinsight to other factors that may affect a user's interactions beyondthe location of the user within the MPS. Context data in accordance withseveral embodiments can include (but is not limited to) location data,time data, user data, and/or system data. Examples of such data caninclude a probability matrix of nearest devices, time of day, day ofweek, user ID, user preferences, device configuration, currently playingcontent, etc.

Process 2400 identifies (2420) a true user interaction. Userinteractions can be used as a source of ground truth for a machinelearning prediction model. In a variety of embodiments, userinteractions can include interactions with a SONOS device (e.g., tapsone or more of the physical buttons on a SONOS player), voiceinteractions with a SONOS device (e.g., a SONOS player that responds toa voice command), and/or responses to system notifications (e.g., apop-up via the Sonos app saying “What speaker are you closest to?”).User responses in accordance with certain embodiments can include (butare not limited to) selecting a target speaker for playing content froma GUI of a controller device, stopping all playback in the MPS, etc.True user interactions in accordance with numerous embodiments caninclude the non-performance of a predicted interaction (e.g., when aprediction model predicts that a user will initiate playback on akitchen speaker, but the user takes no action at all).

Process 2400 identifies (2422) a predicted user interaction using aprediction model. Prediction models in accordance with numerousembodiments can be trained to take in context data and to predict adesired user interaction. In a variety of embodiments, context dataincludes a probability matrix that is computed as described in examplesabove. In several embodiments, prediction models are pre-trained basedon general data and can then be refined based on local data specific toa user or household. Prediction models in accordance with a variety ofembodiments can be used to predict various elements of a userinteraction, including (but not limited to) a target playback device tobe used, specific content to be played on a playback device, modifyingthe volume of playback, transferring playback between playback devices,etc.

Process 2400 generates (2425) training data based on the true userinteraction and the predicted user interaction. Training data inaccordance with several embodiments can include (but is not limited to)the gathered context data labeled with the true user interaction and/orsome aspect thereof (e.g., a target device). In certain embodiments,training data can be used in an online training process to update theprediction model based on the new training data samples.

Process 2400 updates (2425) the prediction model based on the generatedtraining data. Updating the prediction model in accordance with manyembodiments can include updating weights and/or other parameters of theprediction model based on its ability to predict the true userinteraction. In various embodiments, the prediction model is onlyupdated after a threshold number of training data samples have beengathered. In various embodiments, when a new device is added to an MPSand has no usage information, processes can update the prediction modelwith the new device and provide an initialization probability for thenew device. Initialization probabilities in accordance with manyembodiments can be static initial values to ensure that new devices areat least initially available to a user. In certain embodiments,initialization probabilities can be based on the location of the newdevice (e.g., the initialization probabilities for a new device in theliving room can be initiated with probabilities similar to those ofother nearby speakers). As additional training data samples aregathered, the initialization values can be adjusted based on actualusage.

While specific processes for predicting user interactions are describedabove, any of a variety of processes can be utilized as appropriate tothe requirements of specific applications. In certain embodiments, stepsmay be executed or performed in any order or sequence not limited to theorder and sequence shown and described. In a number of embodiments, someof the above steps may be executed or performed substantiallysimultaneously where appropriate or in parallel to reduce latency andprocessing times. In some embodiments, one or more of the above stepsmay be omitted.

a. Prediction Element

FIG. 25 illustrates an example of a prediction element that trains andpredicts target devices in accordance with an embodiment. Predictionelements in accordance with various embodiments can include (but are notlimited to) controller devices, playback devices, and/or server systems.In this example, prediction element 2500 includes processor 2505,peripherals 2510, network interface 2515, and memory 2520. One skilledin the art will recognize that a particular localization element mayinclude other components that are omitted for brevity without departingfrom the scope of the present disclosure.

The processor 2505 can include (but is not limited to) a processor,microprocessor, controller, or a combination of processors,microprocessor, and/or controllers that performs instructions stored inthe memory 2520 to manipulate data stored in the memory. Processorinstructions can configure the processor 2505 to perform processes inaccordance with certain embodiments.

Peripherals 2510 can include any of a variety of components forcapturing data, such as (but not limited to) cameras, displays, and/orsensors. In a variety of embodiments, peripherals can be used to gatherinputs and/or provide outputs. Network interface 2515 allows predictionelement 2500 to transmit and receive data over a network based upon theinstructions performed by processor 2505. Peripherals and/or networkinterfaces in accordance with many embodiments can be used to gatherinputs (e.g., signals, user inputs, and/or context information) that canbe used to predict user interactions.

Memory 2520 includes a prediction application 2525, training data 2530,and model data 2535. Prediction applications in accordance with severalembodiments can be used to predict user interactions (e.g., targetdevices, desired control actions, etc.) in a networked system ofdevices. In numerous embodiments, training data can include datagenerated at the prediction element based on true and/or predicted userinteractions. Model data in accordance with some embodiments can includeparameters for a machine learning model trained to generateprobabilistic location information based on input signalcharacteristics.

Various parts of described systems and methods for predicting and/ortraining prediction models described herein can be performed on anetworked device and/or remotely, for example, on remote computingdevice(s). In at least some embodiments, any or all of the parts of themethods described herein can be performed on networked device ratherthan at remote computing devices.

Although a specific example of a prediction element 2500 is illustratedin FIG. 25 , any of a variety of such elements can be utilized toperform processes similar to those described herein as appropriate tothe requirements of specific applications in accordance withembodiments.

b. Prediction Application

FIG. 26 illustrates an example of a prediction application in accordancewith an embodiment. Prediction applications in accordance with manyembodiments can be used to predict user interactions based on contextdata, including (but not limited to) localization information, userdata, system state data, etc. Prediction application 2600 includescontext engine 2605, prediction engine 2610, training data generator2615, training engine 2620, and output engine 2625.

Context engines in accordance with various embodiments can gathercontext information that can be used to predict a user's interactions.In some embodiments, context engines can perform localization processesfor a portable device to generate a probability matrix that can be usedas an input to a prediction engine. Context engines in accordance withcertain embodiments of the invention can measure signal patterns betweenvarious devices in a system as a way of providing space state as inputto a prediction engine. In a variety of embodiments, context informationcan include identity information for different users (or devices) in ahome, allowing the prediction model to customize the predictions foreach user. Context engines in accordance with various embodiments candetermine system states (e.g., which devices are playing content, whatcontent is being played, which devices have been used recently, etc.).In certain embodiments, context engines can provide the context to aprediction engine to predict a user's interactions.

In several embodiments, prediction engines can take context informationand predict user interactions based on the context information. Userinteractions in accordance with a variety of embodiments can include(but are not limited to) physical interactions, voice interactions,and/or selections of a target device (e.g., through a GUI of acontroller). In a number of embodiments, prediction engines can includea machine learning model such as (but not limited to) neural networks,adversarial networks, and/or logistic regression models that can betrained to predict (or classify) a user interaction based on a givencontext.

Output engines in accordance with numerous embodiments can providevarious outputs based on predicted user interactions from a predictionengine. In certain embodiments, output engines can provide a list oftarget devices, sorted by a likelihood of being the desired targetdevice for a user interaction, which can be displayed at a controllerdevice to allow a user to have quick access to controls for the mostlikely target devices. Output engines in accordance with a number ofembodiments can transmit control instructions (e.g., to initiateplayback, stop playback, modify volume, etc.) to playback devices in anMPS.

Training data generators in accordance with some embodiments cangenerate training data based on a user's interactions with devices in asystem. In many embodiments, training data generators can performprocesses similar to those of FIG. 24 to generate training data samplesthat include context data and true user interactions.

In many embodiments, training engines can train prediction engines in anonline fashion using training data generated based on user interactionswith an MPS. In a variety of embodiments, training engines can computelosses based on a difference between a predicted user interaction and anactual user interaction. For example, when a prediction engine predictsa user's actual interaction with a confidence level of 0.6, the loss canbe computed as 0.4. Computed losses can be used to update parameters fora model (e.g., through backpropagation to update weights of a neuralnetwork).

Although a specific example of a prediction application 2600 isillustrated in FIG. 26 , any of a variety of localization applicationscan be utilized to perform processes for localizing portable devicessimilar to those described herein as appropriate to the requirements ofspecific applications in accordance with embodiments. In someembodiments, one or more of the above elements may be omitted and/oradditional elements may be added.

c. Example Applications

Predicting target devices can have various applications in an MPS, suchas (but not limited to) automated playback of content at a targetdevice, ranked and/or filtered lists of target devices, etc. Examples ofa graphical user interface (GUI) based on predicted target devices areillustrated in FIGS. 27A-B. FIG. 27A illustrates GUI 2700A in two stages2710 and 2720. The first stage 2710 shows GUI 2700A with controlelements (e.g., control element 2712 for the Living Room and controlelement 2714 for the Kitchen) for controlling playback at differentareas of a home. The second stage 2720 shows GUI 2700A after a targetdevice prediction process. In this example, the target device predictionprocess has predicted (e.g., based on the time of day, user's relativelocation, etc.) that the Kitchen is more likely to be the target device(or region) than the Living Room. In the second stage 2720, thepositions of control element 2712 for the Living Room and controlelement 2714 for the Kitchen have been switched, moving the controlelement 2714 for the Kitchen into the top position.

Similarly, FIG. 27B shows GUI 2700B in two stages 2730 and 2740. Thefirst stage 2730 shows GUI 2700B with control elements (e.g., controlelement 2712 for the Living Room) for controlling playback at differentareas of a home. The control element 2712 for the Living Room has beenhighlighted as the most likely to be the target device. The second stage2740 shows GUI 2700B after a target device prediction process, where thetarget device prediction process has predicted (e.g., based on the timeof day, user's relative location, etc.) that the Dining Room is morelikely to be the target device (or region) than the Living Room. In thesecond stage 2740, the positions of control elements do not change, butnow the control element 2716 for the Dining Room is highlighted insteadof the control element 2712 for the Living Room. GUIs in accordance withsome embodiments can implement various different approaches toemphasizing a target device, such as (but not limited to) displaying alimited (e.g., the top n) list of target devices, reducing the contrastof non-target devices, displaying target devices in a different colorand/or size, placement of controls for predicted target devices withinthe display, etc.

Although specific examples of a GUI are illustrated in FIGS. 27A-B, anyof a variety of such GUIs can be utilized to perform processes similarto those described herein as appropriate to the requirements of specificapplications in accordance with embodiments.

VI. Conclusion

The description above discloses, among other things, various examplesystems, methods, apparatus, and articles of manufacture including,among other components, firmware and/or software executed on hardware.It is understood that such examples are merely illustrative and shouldnot be considered as limiting. For example, it is contemplated that anyor all of the firmware, hardware, and/or software aspects or componentscan be embodied exclusively in hardware, exclusively in software,exclusively in firmware, or in any combination of hardware, software,and/or firmware. Accordingly, the examples provided are not the onlyway(s) to implement such systems, methods, apparatus, and/or articles ofmanufacture.

In addition to the examples described herein with respect to stationaryplayback devices, embodiments of the present technology can be appliedto headphones, earbuds, or other in- or over-ear playback devices. Forexample, such in- or over-ear playback devices can includenoise-cancellation functionality to reduce the user's perception ofoutside noise during playback. In some embodiments, noise classificationcan be used to modulate noise cancellation under certain conditions. Forexample, if a user is listening to music with noise-cancellingheadphones, the noise cancellation feature may be temporarily disabledor down-regulated when a user's doorbell rings. Alternatively oradditionally, the playback volume may be adjusted based on detection ofthe doorbell chime. By detecting the sound of the doorbell (e.g., bycorrectly classifying the doorbell based on received sound metadata),the noise cancellation functionality can be modified so that the user isable to hear the doorbell even while wearing noise-cancellingheadphones. Various other approaches can be used to modulate performanceparameters of headphones or other such devices based on the noiseclassification techniques described herein.

The specification is presented largely in terms of illustrativeenvironments, systems, procedures, steps, logic blocks, processing, andother symbolic representations that directly or indirectly resemble theoperations of data processing devices coupled to networks. These processdescriptions and representations are typically used by those skilled inthe art to most effectively convey the substance of their work to othersskilled in the art. Numerous specific details are set forth to provide athorough understanding of the present disclosure. However, it isunderstood to those skilled in the art that certain embodiments of thepresent disclosure can be practiced without certain, specific details.In other instances, well known methods, procedures, components, andcircuitry have not been described in detail to avoid unnecessarilyobscuring aspects of the embodiments. Accordingly, the scope of thepresent disclosure is defined by the appended claims rather than theforgoing description of embodiments.

When any of the appended claims are read to cover a purely softwareand/or firmware implementation, at least one of the elements in at leastone example is hereby expressly defined to include a tangible,non-transitory medium such as a memory, DVD, CD, Blu-ray, and so on,storing the software and/or firmware.

The invention claimed is:
 1. A method for modifying a playback systemcomprising a plurality of playback devices, the method comprising:measuring a first signal pattern for wireless signals between theplurality of playback devices; measuring a second signal pattern for thewireless signals after measuring the first signal pattern between theplurality of playback devices; determining an updated state of aphysical space between devices in the playback system based ondifferences between the second signal pattern and the first signalpattern, wherein determining the updated state comprises estimatingpositions of a set of one or more individuals in a physical spacebetween the plurality of playback devices based on the differencesbetween the second signal pattern and the first signal pattern; andmodifying state variables of one or more devices in the playback systembased on the updated state of the physical space between the devices inthe playback system.
 2. The method of claim 1, wherein the first signalpattern is a baseline signal pattern for a physical space between theplurality of playback devices, where the baseline signal patterncomprises a signal pattern measured at a particular time of day.
 3. Themethod of claim 1, wherein modifying the state variables of playbackdevices of the playback system is based on estimated positions of theset of one or more individuals in the physical space between the devicesin the playback system.
 4. The method of claim 1, further comprisinglearning location information for signal patterns, wherein: measuringsignal patterns consistent with a location of an individual is donewithout need for an additional sensor; and estimating positions of theset of one or more individuals in the physical space is based on learnedlocation information.
 5. The method of claim 4, wherein learninglocation information for signal patterns comprises: measuring aplurality of signal patterns of the physical space at a plurality oftime instances; localizing an individual in the physical space at eachtime instance; and associating a location of the individual with acorresponding signal pattern; wherein estimating the positions of theset of one or more individuals comprises: matching the second signalpattern to a particular signal pattern of the plurality of signalpatterns; and estimating a location for the set of one or moreindividuals based on at least one associated location for the particularsignal pattern.
 6. The method of claim 5, wherein localizing anindividual comprises localizing a portable device associated with theindividual.
 7. The method of claim 5, wherein localizing an individualcomprises receiving input from the individual that indicates a locationof the individual within the physical space.
 8. A non-transitory machinereadable medium containing processor instructions for managing aplayback system comprising a plurality of playback devices, whereexecution of the instructions by a processor causes the processor toperform a process comprising: receiving information indicative of afirst signal pattern for wireless signals between the plurality ofplayback devices; receiving information indicative of a second signalpattern for the wireless signals between the plurality of playbackdevices; determining an updated state of a physical space betweendevices in the playback system based on differences between the secondsignal pattern and the first signal pattern, wherein determining theupdated state comprises estimating positions of a set of one or moreindividuals in a physical space between the plurality of playbackdevices based on the differences between the second signal pattern andthe first signal pattern; and modifying state variables of one or moreplayback devices of the playback system based on the updated state ofthe physical space between the devices in the playback system.
 9. Thenon-transitory machine readable medium of claim 8 further comprisingrepeatedly detecting motion in a physical space between the plurality ofplayback devices, wherein the first signal pattern is measured whenthere is no motion measured in the physical space.
 10. Thenon-transitory machine readable medium of claim 8, wherein modifying theplayback system comprises modifying a set of one or more playbackparameters for audio content provided at the plurality of playbackdevices, wherein the set of one or more playback parameters comprises atleast one of the group consisting of equalizer settings, volume, bass,treble, balance, and fade.
 11. The non-transitory machine readablemedium of claim 8, wherein the first signal pattern is a baseline signalpattern for a physical space between the plurality of playback devices,wherein the instructions further comprise periodically updating thebaseline signal pattern.
 12. The non-transitory machine readable mediumof claim 11, wherein updating the baseline signal pattern comprisescomputing an average pattern from signal strengths measured at varioustimes of day.
 13. The non-transitory machine readable medium of claim11, wherein updating the baseline signal pattern comprises: detecting alack of activity in the playback system; measuring a third pattern ofwireless signals between the plurality of playback devices; and updatingthe baseline signal pattern with the third pattern.
 14. Thenon-transitory machine readable medium of claim 8, wherein the pluralityof playback devices comprises a center speaker device, a right speakerdevice, and a left speaker device.
 15. A playback device of a playbacksystem comprising a plurality of playback devices, the playback devicecomprising: a network interface; a set of one or more processors; and anon-transitory machine readable medium containing processorinstructions, where execution of the instructions by a processor causesthe processor to perform a process comprising: receiving informationindicative of a first signal pattern for wireless signals between theplurality of playback devices; receiving information indicative of asecond signal pattern for the wireless signals between the plurality ofplayback devices; determining an updated state of a physical spacebetween devices in the playback system based on differences between thesecond signal pattern and the first signal pattern, wherein determiningthe updated state comprises estimating positions of a set of one or moreindividuals in a physical space between the plurality of playbackdevices based on the differences between the second signal pattern andthe first signal pattern; and modifying state variables of one or moreplayback devices of the playback system based on the updated state ofthe physical space between the devices in the playback system.
 16. Theplayback device of claim 15, wherein determining an updated statecomprises detecting a change in at least one of the group consisting ofa location and orientation of at least one playback device of theplurality of playback devices.
 17. The playback device of claim 16,wherein the plurality of playback devices comprises at least two primaryplayback devices and a set of one or more secondary playback devices,wherein the first and second signal patterns comprise signal strengthsmeasured between each of the at least two primary playback devices andthe set of one or more secondary playback devices, wherein detecting achange comprises determining a repositioning of the set of one or moresecondary playback devices.
 18. The playback device of claim 16, whereinthe plurality of playback devices comprises a primary playback deviceand a set of one or more secondary playback devices, wherein the firstand second signal patterns comprise signal strengths measured between atleast one radio chain of the primary playback device and each of aplurality of radio chains on each of the one or more secondary playbackdevices, wherein detecting a change comprises determining arepositioning of the set of one or more secondary playback devices. 19.The playback device of claim 15, wherein modifying the playback systemcomprises: determining whether a difference exceeds a threshold; whenthe difference exceeds a threshold, performing a recalibration process;and when the difference does not exceed a threshold, providing aninstruction to reposition at least one playback device of the playbacksystem.
 20. The playback device of claim 15, wherein measuring the firstand second signal patterns comprises capturing a statistical measure ofwireless signal strengths over a period of time.
 21. The playback deviceof claim 15, wherein modifying the playback system comprises:determining a predicted target action based on a machine learning model,wherein the machine learning model is trained based on states of theplayback system and a history of device interactions; and performing thepredicted target action.
 22. The method of claim 1, wherein a physicalentity is selected from the group consisting of a person and an object.23. The non-transitory machine readable medium of claim 8, wherein aphysical entity is selected from the group consisting of a person and anobject.
 24. The playback device of claim 15, wherein a physical entityis selected from the group consisting of a person and an object.